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Altaf, Muhammad Faheem, Muhammad Waseem Iqbal, Ghulam Ali, et al. "Neural network-based ensemble approach for multi-view facial expression recognition." PLOS ONE 20, no. 3 (2025): e0316562. https://doi.org/10.1371/journal.pone.0316562.
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In this paper, we developed a pose-aware facial expression recognition technique. The proposed technique employed K nearest neighbor for pose detection and a neural network-based extended stacking ensemble model for pose-aware facial expression recognition. For pose-aware facial expression classification, we have extended the stacking ensemble technique from a two-level ensemble model to three-level ensemble model: base-level, meta-level and predictor. The base-level classifier is the binary neural network. The meta-level classifier is a pool of binary neural networks. The outputs of binary neural networks are combined using probability distribution to build the neural network ensemble. A pool of neural network ensembles is trained to learn the similarity between multi-pose facial expressions, where each neural network ensemble represents the presence or absence of a facial expression. The predictor is the Naive Bayes classifier, it takes the binary output of stacked neural network ensembles and classifies the unknown facial image as one of the facial expressions. The facial concentration region was detected using the Voila-Jones face detector. The Radboud faces database was used for stacked ensembles’ training and testing purpose. The experimental results demonstrate that the proposed technique achieved 90% accuracy using Eigen features with 160 stacked neural network ensembles and Naive Bayes classifier. It demonstrates that the proposed techniques performed significantly as compare to state of the art pose-ware facial expression recognition techniques.
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Gunasekaran, Hemalatha, Angelin Gladys, Deepa kanmani, Rex Macedo, and Wilfred Blessing N R. "Brain Stroke Prediction Using Stacked Ensemble Model." Jurnal Kejuruteraan 36, no. 4 (2024): 1759–68. http://dx.doi.org/10.17576/jkukm-2024-36(4)-38.
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Stroke is a potentially fatal illness that requires emergency care. There is a greater chance that the patient will recover and resume their regular life when they receive treatment and diagnosis as soon as feasible. Artificial Intelligence has the potential to significantly impact stroke diagnosis and facilitate prompt patient treatment for physicians. Machine learning can be utilized in stroke prediction by evaluating huge volumes of patient data and detecting patterns and risk variables that may contribute to the likelihood of a stroke. In this study, we explored a stacked ensemble model that uses four base models—Decision Tree, XGBoost, RandomForest, and ExtraTree classifiers to predict the stroke. We discovered that the accuracy of the stacked ensemble model was 96.35%, higher than that of the traditional machine-learning models, other ensemble models, and ANN model.
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AKÇAY, Selma, Selim BUYRUKOĞLU, and Ünal AKDAĞ. "Stacked Heterogeneous Ensemble Learning Model in Mixed Convection Heat Transfer from a Vertically Oscillating Flat Plate." Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 6, no. 1 (2023): 635–54. http://dx.doi.org/10.47495/okufbed.1100651.
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In this study, the effects of mixed convection heat transfer from a moving vertical flat plate with an experimental and stacked heterogeneous ensemble learning approach are analyzed. In the experimental work, the effects on both natural and forced convection of dimensionless oscillation amplitude (Ao), dimensionless oscillation frequency (Wo) and Rayleigh number (Ra) are investigated. In the experiments, the vertical movement of the plate is provided by a flywheel-motor assembly. The average Nusselt numbers (Nu) on the fixed plate and the moving plate surface were obtained. Additionally, this study is focused on the prediction of heat transfer of a moving flat plate using single-based algorithms (Gradient Boosting, AdaBoost, Multilayer Per-ceptron) and a stacked heterogeneous ensemble learning model. The statistical per-formance of the single-based algorithms and the stacked ensemble model is meas-ured in the prediction of mixed convection heat transfer. The results show that the stacked-based ensemble learning model yielded the MSE = 2.01, RMSE = 1.42, MAE = 1.1 and R2 = 0.99 values. Overall, this study reveals that the proposed stacked en-semble machine learning model can be used successfully for modeling convection heat transfer of a moving plate.
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Novaes de Amorim, Arthur, Rob Deardon, and Vineet Saini. "A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments." PLOS ONE 16, no. 3 (2021): e0241725. http://dx.doi.org/10.1371/journal.pone.0241725.
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Accurate and reliable short-term forecasts of influenza-like illness (ILI) visit volumes at emergency departments can improve staffing and resource allocation decisions within hospitals. In this paper, we developed a stacked ensemble model that averages the predictions from various competing methodologies in the current frontier for ILI-related forecasts. We also constructed a back-of-the-envelope prediction interval for the stacked ensemble, which provides a conservative characterization of the uncertainty in the stacked ensemble predictions. We assessed the accuracy and reliability of our model with 1 to 4 weeks ahead forecast targets using real-time hospital-level data on weekly ILI visit volumes during the 2012-2018 flu seasons in the Alberta Children’s Hospital, located in Calgary, Alberta, Canada. Our results suggest the forecasting performance of the stacked ensemble meets or exceeds the performance of the individual models over all forecast targets.
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Folake, Akinbohun, Akinbohun Ambrose, and Oyinloye Oghenerukevwe E. "Stacked Ensemble Model for Hepatitis in Healthcare System." International Journal of Computer and Organization Trends 9, no. 4 (2019): 25–29. http://dx.doi.org/10.14445/22492593/ijcot-v9i4p305.
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Faraji, Mohammad Amin, Alireza Shooshtari, and Ayman El-Hag. "Stacked Ensemble Regression Model for Prediction of Furan." Energies 16, no. 22 (2023): 7656. http://dx.doi.org/10.3390/en16227656.
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Furan tests provide a non-intrusive and cost-effective method of estimating the degradation of paper insulation, which is critical for ensuring the reliability of power grids. However, conducting routine furan tests can be expensive and challenging, highlighting the need for alternative methods, such as machine learning algorithms, to predict furan concentrations. To establish the generalizability and robustness of the furan prediction model, this study investigates two distinct datasets from different geographical locations, Utility A and Utility B. Three scenarios are proposed: in the first scenario, a round-robin cross-validation method was used, with 75% of the data for training and the remaining 25% for testing. The second scenario involved training the model entirely on Utility A and testing it on Utility B. In the third scenario, the datasets were merged, and round-robin cross-validation was applied, similar to the first scenario. The findings reveal the effectiveness of machine learning algorithms in predicting furan concentrations, and particularly the stacked generalized ensemble method, offering a non-intrusive and cost-effective alternative to traditional testing methods. The results could significantly impact the maintenance strategies of power and distribution transformers, particularly in regions where furan testing facilities are not readily available.
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Maigari, Aminu, Zurinahni Zainol, and Chew Xinying. "Multi-modal Stacked Ensemble Model for Breast Cancer Prognosis Prediction." Statistics, Optimization & Information Computing 13, no. 3 (2024): 1013–34. https://doi.org/10.19139/soic-2310-5070-2100.
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Breast cancer (BC) is a global health challenge that affects millions of women worldwide and leads to significant mortality. Recent advancements in next-generation sequencing technology have enabled comprehensive diagnosis and prognosis determination using multiple data modalities. Deep learning methods have shown promise in utilizing these multimodal data sources, outperforming single-modal models. However, integrating these heterogeneous data sources poses significant challenges in clinical decision-making. This study proposes an optimized multimodal CNN for a stacked ensemble model (OMCNNSE) for breast cancer prognosis. Our novel method involves the integration of the Tug of War (TWO) algorithm to optimize the hyperparameters of a convolutional neural network (CNN), enhancing feature extraction from three distinct multimodal datasets: clinical profile data, copy number alteration (CNA), and gene expression data. Specifically, we employ the TWO algorithm to optimize separate CNN models for each dataset, identifying optimal values for the hyperparameters. We then trained the three baseline CNN models using the optimized values through 10-fold crossvalidation. Finally, we utilize an ensemble learning approach to integrate the models’ predictions and apply an SVM classifier for the final prediction. To evaluate the proposed method, we conducted experiments on the METABRIC breast cancer dataset comprising diverse patient profiles. Our results demonstrated the effectiveness of the OMCNNSE approach for predicting breast cancer prognosis. The model achieved high AUC, accuracy, sensitivity, precision, and MCC, outperforming traditional single-modal models and other state-of-the-art methods.
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Sung, Chih-Wei, Joshua Ho, Cheng-Yi Fan, et al. "Prediction of high-risk emergency department revisits from a machine-learning algorithm: a proof-of-concept study." BMJ Health & Care Informatics 31, no. 1 (2024): e100859. http://dx.doi.org/10.1136/bmjhci-2023-100859.
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BackgroundHigh-risk emergency department (ED) revisit is considered an important quality indicator that may reflect an increase in complications and medical burden. However, because of its multidimensional and highly complex nature, this factor has not been comprehensively investigated. This study aimed to predict high-risk ED revisit with a machine-learning (ML) approach.MethodsThis 3-year retrospective cohort study assessed adult patients between January 2019 and December 2021 from National Taiwan University Hospital Hsin-Chu Branch with high-risk ED revisit, defined as hospital or intensive care unit admission after ED return within 72 hours. A total of 150 features were preliminarily screened, and 79 were used in the prediction model. Deep learning, random forest, extreme gradient boosting (XGBoost) and stacked ensemble algorithm were used. The stacked ensemble model combined multiple ML models and performed model stacking as a meta-level algorithm. Confusion matrix, accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUROC) were used to evaluate performance.ResultsAnalysis was performed for 6282 eligible adult patients: 5025 (80.0%) in the training set and 1257 (20.0%) in the testing set. High-risk ED revisit occurred for 971 (19.3%) of training set patients vs 252 (20.1%) in the testing set. Leading predictors of high-risk ED revisit were age, systolic blood pressure and heart rate. The stacked ensemble model showed more favourable prediction performance (AUROC 0.82) than the other models: deep learning (0.69), random forest (0.78) and XGBoost (0.79). Also, the stacked ensemble model achieved favourable accuracy and specificity.ConclusionThe stacked ensemble algorithm exhibited better prediction performance in which the predictions were generated from different ML algorithms to optimally maximise the final set of results. Patients with older age and abnormal systolic blood pressure and heart rate at the index ED visit were vulnerable to high-risk ED revisit. Further studies should be conducted to externally validate the model.
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Mahajan, Asmita, Nonita Sharma, Silvia Aparicio-Obregon, et al. "A Novel Stacking-Based Deterministic Ensemble Model for Infectious Disease Prediction." Mathematics 10, no. 10 (2022): 1714. http://dx.doi.org/10.3390/math10101714.
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Infectious Disease Prediction aims to anticipate the aspects of both seasonal epidemics and future pandemics. However, a single model will most likely not capture all the dataset’s patterns and qualities. Ensemble learning combines multiple models to obtain a single prediction that uses the qualities of each model. This study aims to develop a stacked ensemble model to accurately predict the future occurrences of infectious diseases viewed at some point in time as epidemics, namely, dengue, influenza, and tuberculosis. The main objective is to enhance the prediction performance of the proposed model by reducing prediction errors. Autoregressive integrated moving average, exponential smoothing, and neural network autoregression are applied to the disease dataset individually. The gradient boosting model combines the regress values of the above three statistical models to obtain an ensemble model. The results conclude that the forecasting precision of the proposed stacked ensemble model is better than that of the standard gradient boosting model. The ensemble model reduces the prediction errors, root-mean-square error, for the dengue, influenza, and tuberculosis dataset by approximately 30%, 24%, and 25%, respectively.
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Wei, Fandi, and Hongming Zhou. "Credit Default Prediction via Stacked Logistic Regression Ensemble." Advances in Economics, Management and Political Sciences 185, no. 1 (2025): 24–34. https://doi.org/10.54254/2754-1169/2025.lh23964.
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Traditional single-risk assessment models perform inadequately with imbalanced data. This study aims to construct a high-precision and stable credit default prediction model to address both data imbalance and model generalization issues. This study aims to develop a high-precision credit default prediction model to address the challenges of imbalanced data and model generalization. By applying SMOTEENN hybrid sampling to optimize data distribution and integrating it with a Stacked Logistic Regression Ensemble (LR-Stacking), the model combines the predictive advantages of XGBoost, CatBoost, and Random Forest through a meta-learning layer. This approach effectively enhances the recall rate for imbalanced data and improves model generalization. Empirical results demonstrate notable improvements: the model achieves a recall rate of 0.72 for default samples, maintains an AUC score of 0.7341, balances risk coverage and prediction precision, and particularly enhances model stability. By integrating the Stacked Logistic Regression Ensemble Model (i.e., LR-Stacking), we use a meta-learning layer to combine the predictive strengths of XGBoost, CatBoost, and Random Forest. This approach ultimately enhances recall rate and model generalization performance in imbalanced data scenarios.
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Li, Ruixuan. "Bank Customer Churn Prediction Based on Stacking Model." Advances in Economics, Management and Political Sciences 185, no. 1 (2025): 42–51. https://doi.org/10.54254/2754-1169/2025.lh23930.
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With the increasingly fierce competition in the financial industry, customer churn prediction has become a key research topic. Accurate prediction of which customers are more likely to churn can help banks take timely retention measures to reduce business losses. This paper adopts a data-driven approach and uses the public bank customer churn dataset to deeply analyze the distribution of data characteristics and deal with the problem of data imbalance, and proposes a customer churn prediction method based on stacked ensemble model. In this study, random forest, XGBoost, CatBoost and LightGBM were used as the basic model, and XGBoost was used as the meta-learner to establish a two-layer stacked ensemble framework. Compared with the traditional single model and simple ensemble methods, the experimental results show that the proposed method is significantly ahead in Accuracy, Recall, AUC, F1-score and other indicators, which verifies its advanced and precise capabilities in customer churn prediction.
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Karuppaiah, Sivasankar, and N. P. Gopalan. "Enhanced Churn Prediction Using Stacked Heuristic Incorporated Ensemble Model." Journal of Information Technology Research 14, no. 2 (2021): 174–86. http://dx.doi.org/10.4018/jitr.2021040109.
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In a rapidly growing industry like telecommunications, customer churn prediction is a crucial challenge affecting the sustainability of the business as a whole. The fact that retaining a customer is more profitable than acquiring new customers is important to predict potential churners and present them with offers to prevent them from churning. This work presents a stacked CLV-based heuristic incorporated ensemble (SCHIE) to enable identification of potential churners so as to provide them with offers that can eventually aid in retaining them. The proposed model is composed of two levels of prediction followed by a recommendation to reduce customer churn. The first level involves identifying effective models to predict potential churners. This is followed by result segregation, CLV-based prediction, and user shortlisting for offers. Experimental results indicate high efficiencies in predicting potential churners and non-churners. The proposed model is found to reduce the overall loss by up to 50% in comparison to state-of-the-art models.
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Madhu, G., B. Lalith Bharadwaj, Rohit Boddeda, et al. "Deep Stacked Ensemble Learning Model for COVID-19 Classification." Computers, Materials & Continua 70, no. 3 (2022): 5467–69. http://dx.doi.org/10.32604/cmc.2022.020455.
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Ren, Na, Xin Zhao, and Xin Zhang. "Mortality prediction in ICU Using a Stacked Ensemble Model." Computational and Mathematical Methods in Medicine 2022 (November 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/3938492.
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Artificial intelligence (AI) technology has huge scope in developing models to predict the survival rate of critically ill patients in the intensive care unit (ICU). The availability of electronic clinical data has led to the widespread use of various machine learning approaches in this field. Innovative algorithms play a crucial role in boosting the performance of models. This study uses a stacked ensemble model to predict mortality in ICU by incorporating the clinical severity scoring results, in which several machine learning algorithms are employed to compare the performance. The experimental results show that the stacked ensemble model achieves good performance compared with the model without integrating the severity scoring results, which has the area under curve (AUC) of 0.879 and 0.862, respectively. To improve the performance of prediction, two feature subsets are obtained based on different feature selection techniques, labeled as SetS and SetT. Evaluation performances show that the SEM based on the SetS achieves a higher AUC value (0.879 and 0.860). Finally, the SHapley Additive exPlanations (SHAP) analysis is employed to interpret the correlation between the risk features and the outcome.
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Nam, Tran Quy. "Evaluation of Stacked Ensemble Model on Weather Image Recognition." Procedia Computer Science 234 (2024): 1664–71. http://dx.doi.org/10.1016/j.procs.2024.03.171.
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Kim, Woosung, and Jengei Hong. "Stacked Ensemble Model for the Automatic Valuation of Residential Properties in South Korea: A Case Study on Jeju Island." Land 13, no. 9 (2024): 1436. http://dx.doi.org/10.3390/land13091436.
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While the use of machine learning (ML) in automated real estate valuation is growing, research on stacking ML models into ensembles remains limited. In this paper, we propose a stacked ensemble model for valuing residential properties. By applying our models to a comprehensive dataset of residential real estate transactions from Jeju Island, spanning 2012 to 2021, we demonstrate that the predictive power of ML-based models can be enhanced. Our findings indicate that the stacked ensemble model, which combines predictions using ridge regression, outperforms all individual algorithms across multiple metrics. This model not only minimizes prediction errors but also provides the most stable and consistent results, as evidenced by the lowest standard deviation in both absolute errors and absolute percentage errors. Additionally, we employed the decision tree method to analyze the conditions under which specific features yield more accurate results or less reliable outcomes. It was observed that both the size and age of an apartment significantly impact prediction performance, with smaller and older complexes exhibiting lower accuracy and higher error rates.
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Alkhammash, Eman H., Myriam Hadjouni, and Ahmed M. Elshewey. "A Hybrid Ensemble Stacking Model for Gender Voice Recognition Approach." Electronics 11, no. 11 (2022): 1750. http://dx.doi.org/10.3390/electronics11111750.
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Gender recognition by voice is a vital research subject in speech processing and acoustics, as human voices have many remarkable characteristics. Voice recognition is beneficial in a variety of applications, including mobile health care systems, interactive systems, crime analysis, and recognition systems. Several algorithms for voice recognition have been developed, but there is still potential for development in terms of the system’s accuracy and efficiency. Recent research has focused on combining ensemble learning with a variety of machine learning models in order to create more accurate classifiers. In this paper, a stacked ensemble for gender voice recognition model is presented, using four classifiers, namely, k-nearest neighbor (KNN), support vector machine (SVM), stochastic gradient descent (SGD), and logistic regression (LR) as base classifiers and linear discriminant analysis (LDA) as meta classifier. The dataset used includes 3168 instances and 21 features, where 20 features are the predictors, and one feature is the target. Several prediction evaluation metrics, including precision, accuracy, recall, F1 score, and area under the receiver operating characteristic curve (AUC), were computed to verify the execution of the proposed model. The results obtained illustrated that the stacked model achieved better results compared to other conventional machine learning models. The stacked model achieved high accuracy with 99.64%.
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Olaniyan, Olatayo Moses, Olusogo Julius Adetunji, and Adedire Marquis Fasanya. "Development of a Model for the Prediction of Lumpy Skin Diseases using Machine Learning Techniques." ABUAD Journal of Engineering Research and Development (AJERD) 6, no. 2 (2023): 100–112. http://dx.doi.org/10.53982/ajerd.2023.0602.10-j.
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Lumpy skin diseases virus (LSDV) is a dangerous and contagious diseases that are mostly common in Sub-Saharan African, South Eastern Europe, South Asia and as well as Middle East, China. LSDV is transmitted through blood sucking insects which are double stranded DNA virus and belong to the family of Capri poxvirus genus family. The recent study proved and clarified that lumpy skin diseases viruses (LSDV) affected mostly cattle and buffalo in Africa, Asia and Europe with population of 29 966, 8 837 and 2 471 outbreaks respectively, between the years 2005 – 2021. Different machine learning approaches have been adopted for the prediction of lumpy skin diseases. An enhanced model was developed to improve the predictive performance of existing model and also, compared the performance of stacked ensemble of single classifiers with respect to optimized artificial neural network. The implementation was done with python 3.7 on Core i5, 16G RAM Intel hardware. The single classifiers are decision tree (DT), k-nearest neighbor, random forest (RF) and support vector machine (SVM). A feature wiz feature selection technique was adopted on lumpy skin diseases dataset coupled with the parameters tuning of the model before classification. Both stacked ensemble and optimized artificial neural network model outperformed the existing model. Stacked ensemble model gives accuracy, precision, f1-score and recall of 97.69%, 98.44%, 98.93% and 98.68% respectively. The results also showed that optimized artificial neural networks of 200 epochs outperformed stacked ensemble classifiers with accuracy of 98.89% and 98.66% of training and validation respectively. The developed model in a real world would assist in reducing the occurrence of lumpy skin diseases.
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Raboy, Love Jhoye Moreno, and Attaphongse Taparugssanagorn. "Verse1-Chorus-Verse2 Structure: A Stacked Ensemble Approach for Enhanced Music Emotion Recognition." Applied Sciences 14, no. 13 (2024): 5761. http://dx.doi.org/10.3390/app14135761.
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In this study, we present a novel approach for music emotion recognition that utilizes a stacked ensemble of models integrating audio and lyric features within a structured song framework. Our methodology employs a sequence of six specialized base models, each designed to capture critical features from distinct song segments: verse1, chorus, and verse2. These models are integrated into a meta-learner, resulting in superior predictive performance, achieving an accuracy of 96.25%. A basic stacked ensemble model was also used in this study to independently run the audio and lyric features for each song segment. The six-input stacked ensemble model surpasses the capabilities of models analyzing song parts in isolation. The pronounced enhancement underscores the importance of a bimodal approach in capturing the full spectrum of musical emotions. Furthermore, our research not only opens new avenues for studying musical emotions but also provides a foundational framework for future investigations into the complex emotional aspects of music.
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Akinbohun, Folake, Ambrose Akinbohun, Adekunle Daniel, and Oghenerukevwe Elohor Ojajuni. "Diagnosis of Head and Neck Cancer in Developing Countries Using a Stacked Ensemble Model." European Journal of Engineering Research and Science 5, no. 9 (2020): 1097–101. http://dx.doi.org/10.24018/ejers.2020.5.9.2095.
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Head and neck cancers (HNC) are indicated when cells grow abnormally. The incidence of HNC is on the increase owing to several factors. There is often late presentation that can result in loss of lives (mortality) especially in Africa due to paucity of specialists. These challenges prompted the development of a stacked ensemble model for diagnosis of HNC to facilitate prompt referral. The data were collected which consists of 1473 instances with 18 features. Information Gain was used for selecting important features and three supervised learning algorithms were deployed for the base learners: Decision Tree (C4.5), K-Nearest Neighbors and Naïve Bayes. The predictions of the base learners were combined and passed to meta learners: Logistic Model Tree (LMT). The result showed that Information Gain method with stacked LMTwas 95.11%. It was deduced that both Information Gain with stacked MLR produced higher accuracy that the base learners’ results. Hence, this stacked model can be used for diagnosis of HNC in healthcare systems.
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Akinbohun, Folake, Ambrose Akinbohun, Adekunle Daniel, and Oghenerukevwe Elohor Ojajuni. "Diagnosis of Head and Neck Cancer in Developing Countries Using a Stacked Ensemble Model." European Journal of Engineering and Technology Research 5, no. 9 (2020): 1097–101. http://dx.doi.org/10.24018/ejeng.2020.5.9.2095.
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Head and neck cancers (HNC) are indicated when cells grow abnormally. The incidence of HNC is on the increase owing to several factors. There is often late presentation that can result in loss of lives (mortality) especially in Africa due to paucity of specialists. These challenges prompted the development of a stacked ensemble model for diagnosis of HNC to facilitate prompt referral. The data were collected which consists of 1473 instances with 18 features. Information Gain was used for selecting important features and three supervised learning algorithms were deployed for the base learners: Decision Tree (C4.5), K-Nearest Neighbors and Naïve Bayes. The predictions of the base learners were combined and passed to meta learners: Logistic Model Tree (LMT). The result showed that Information Gain method with stacked LMTwas 95.11%. It was deduced that both Information Gain with stacked MLR produced higher accuracy that the base learners’ results. Hence, this stacked model can be used for diagnosis of HNC in healthcare systems.
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Kasthuriarachchi, K. T. Sanvitha, and Sidath R. Liyanage. "Three-Layer Stacked Generalization Architecture With Simulated Annealing for Optimum Results in Data Mining." International Journal of Artificial Intelligence and Machine Learning 11, no. 2 (2021): 1–27. http://dx.doi.org/10.4018/ijaiml.20210701.oa10.
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The combination of different machine learning models to a single prediction model usually improves the performance of the data analysis. Stacking ensembles are one of such approaches to build a high performance classifier that can be applied to various contexts of data mining. This study proposes an enhanced stacking ensemble by collating a few machine learning algorithms with two layered meta classifications to address the limitations of existing stacking architecture to utilize Simulated Annealing Algorithm to optimize the classifier configuration in order to reach the best prediction accuracy. The proposed method significantly outperformed three general stacking ensembles of two layers that have been executed using the meta classifiers utilized in the proposed architecture. These assessments have been statistically proven at a 95% confidence level. The novel stacking ensemble has also outperformed the existing ensembles named; Adaboost algorithm, Gradient boosting algorithm, XGBoost classifier and bagging classifiers as well.
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Han, Qinghua, Qian Ma, Dazhi Dang, and Jie Xu. "Modal Parameters Prediction and Damage Detection of Space Grid Structure under Environmental Effects Using Stacked Ensemble Learning." Structural Control and Health Monitoring 2023 (March 4, 2023): 1–24. http://dx.doi.org/10.1155/2023/5687265.
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A stacked ensemble learning model is developed to predict the modal parameters of space grid steel structures under environmental effects. Potential damage is detected via statistical analysis of the prediction residuals. For this purpose, five standalone heterogeneous machine learning models were trained for predicting natural frequencies; each model used the principal components of the environmental data as input parameters. Next, a stacked ensemble learner was built using the outputs of the five standalone models as its inputs. Finally, a damage indicator combining the predicted residuals of multiple orders of natural frequencies is proposed and statistically analyzed for accurate damage detection. To verify the effectiveness of the proposed method, a space grid model was created in the field environment and measured for a period. Dynamic and environmental data were collected, such as ambient temperature, humidity, wind speed and direction, and structural surface temperature. An automated procedure of the covariance-driven stochastic subspace identification method was conducted to identify bulk mode. The environmental dependence of the natural frequencies, damping ratios, and vibration modes was analyzed. Then, the method was validated based on short-term monitoring data from the baseline health state and unknown future states. The results show that the natural frequencies and damping ratios of space grid structures fluctuate significantly on a daily basis due to environmental influences. Stacked ensemble learning utilizes predictions from multiple heterogeneous models to produce a better predictive model. The statistical analysis of the prediction residuals by ensemble learning effectively removes the environmental influences, allowing for timely structural damage detection.
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Olalere, Morufu, Gilbert I. O. Aimufua, Muhammad Umar Abdullahi, and Bako Halilu Egga. "Ensemble-based predictive model for crop recommendation." Dutse Journal of Pure and Applied Sciences 10, no. 2c (2024): 391–409. http://dx.doi.org/10.4314/dujopas.v10i2c.36.
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Agriculture is a vital industry that supplies food, textiles, and other basic goods to people globally. Agricultural crop production has a vital role in influencing the economy and the well-being of farmers. Nevertheless, farmers are facing substantial challenges due to the profound changes in environmental conditions. A significant challenge they have is determining the most suitable crop for their specific location that will optimize both production and profitability. Choosing suitable crop types for a certain area may be difficult due to the need for skills and experience in evaluating elements such as soil composition, climatic conditions, moisture levels, precipitation, and temperature. Multiple researchers have devised several approaches to tackle the issue of crop recommendation. Nevertheless, a significant share of these models is specifically tailored for a certain job or are amalgamations that include two or three machine-learning algorithms. These current models have restricted prediction accuracy and elevated rates of false positives, rendering them inappropriate for the intricacy of the job at hand. This study explores the field of precision agriculture with the objective of improving crop recommendation systems via the use of an ensemble-based prediction model. This paper incorporates KNN, Decision Tree, Random Forest, SVM, Naive Bayes, Logistic Regression, and XGBoost as a series of machine learning models. A stacked ensemble prediction model is created by training, evaluating, and comparing the Random Forest classifier with the stacked ensemble prediction model. In contrast to existing methods, the proposed method exhibits exceptionally high accuracy, reaching 99.8%, exceeding the performance of prior studies. Through the application of advanced predictive modeling techniques, this paper demonstrates how agricultural operations can be improved.
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Kumar, Krishna, Hardwari Lal Mandoria, and Rajeev Singh. "Efficient Malware Classification using Transfer Learning and Stacked Ensemble Techniques." International Journal of Mathematical, Engineering and Management Sciences 10, no. 4 (2025): 913–30. https://doi.org/10.33889/ijmems.2025.10.4.044.
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The exponential growth of internet usage and communication devices has led to heightened security vulnerabilities, including the proliferation of malware such as viruses, ransomware, trojans, and spyware. These increasingly sophisticated malware variants pose significant challenges in their detection and classification. The existing visualization-based deep learning approach addresses some of these challenges but often requires extensive computational resources and prolonged training times and is prone to overfitting. This work proposed a transfer learning-based stacked ensemble technique to enhance the efficiency and accuracy of a malware classification model. The six scaled variants of the EfficientNetB0 architecture are selected for their performance on the ImageNet dataset. Their scalability was trained on the Malimg dataset, which comprises 9,339 malware images across 25 categories. Leveraging transfer learning for feature extraction significantly reduced training time and achieved a competitive accuracy of 99.10% within fewer epochs. To further enhance performance, the study employed a stacked ensemble approach by combining the strengths of three high-performing transfer learning models into two ensemble configurations: an average ensemble and a weighted average ensemble. The weighted average ensemble model demonstrated superior performance, achieving a remarkable training accuracy of 99.84% and a validation accuracy of 99.25%. These results underscore the effectiveness of the proposed approach in addressing modern malware classification challenges efficiently.
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Li, Zhichao, Li Tian, Qingchao Jiang, and Xuefeng Yan. "Distributed-ensemble stacked autoencoder model for non-linear process monitoring." Information Sciences 542 (January 2021): 302–16. http://dx.doi.org/10.1016/j.ins.2020.06.062.
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Sudhan Reddy, D. Madhu, and N. Usha Rani. "Crop prediction using an enhanced stacked ensemble machine learning model." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 3 (2025): 1840. https://doi.org/10.11591/ijeecs.v38.i3.pp1840-1850.
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In India, agriculture is a major sector that fulfils the population's food requirements and significantly contributes to the gross domestic product (GDP). The careful selection of crops is fundamental to maximizing agricultural yield, thereby elevating the economic vitality of the farming community. Precision agriculture (PA) leverages weather and soil data to inform crop selection strategies. Conventional machine learning (ML) models such as decision trees (DT), support vector classifier, K-nearest neighbors (KNN), and extreme gradient boost (XGBoost) have been deployed to predict the best crop. However, these model's efficiency is suboptimal in the current circumstances. The enhanced stacked ensemble ML model is a sophisticated meta-model that addresses these limitations. It harnesses the predictive power of individual ML models, stratified in a layered architecture to improve the prediction accuracy. This advanced model has demonstrated a commendable accuracy rate of 93.1% prediction by taking input of 12 soil parameters such as Nitrogen, Phosphorus, Potassium, and weather parameters such as temperature and rainfall, substantially outperforming the accuracies achieved by the individual contributing models. The efficacy of the proposed meta-model in crop selection based on agronomic parameters signifies a substantial advancement, fortifying the economic resilience of India's agriculture.
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Ma, Ting, Xiaofeng Zhang, and Zhexin Miao. "Detection of UAV GPS Spoofing Attacks Using a Stacked Ensemble Method." Drones 9, no. 1 (2024): 2. https://doi.org/10.3390/drones9010002.
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Unmanned aerial vehicles (UAVs) are vulnerable to global positioning system (GPS) spoofing attacks, which can mislead their navigation systems and result in unpredictable catastrophic consequences. To address this issue, we propose a detection method based on stacked ensemble learning that combines convolutional neural network (CNN) and extreme gradient boosting (XGBoost) to detect spoofing signals in the GPS data received by UAVs. First, we applied the synthetic minority oversampling (SMOTE) technique to the dataset to address the issue of class imbalance. Then, we used a CNN model to extract high-level features, combined with the original features as input for the stacked model. The stacked model employs XGBoost as the base learner, which is optimized through five-fold cross-validation, and utilizes logistic regression for the final prediction. Furthermore, we incorporated magnetic field data to enhance the system’s robustness, thereby further improving the accuracy and reliability of GPS spoofing attack detection. Experimental results indicate that the proposed model achieved a high accuracy of 99.79% in detecting GPS spoofing attacks, demonstrating its potential effectiveness in enhancing UAV security.
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Are, M. T. Okorie P. U. Olarinoye G. A. "A Review of Short-Term Electrical Load Forecasting Using Ensemble Stacking Generalization with Artificial Neural Network." Journal of Materials Engineering, Structures and Computation 2, no. 1 (2023): 36–52. https://doi.org/10.5281/zenodo.7760014.
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<em>Electric load forecasting has gained much attention in electricity production due to its important role in electric power system management. Short-term load forecasting (STLF) uses the perception of ensemble learning approaches as a general scheme for educating the prognostic skill of a machine learning model (MLM). STLF is subjected to numerous errors /problems like high bias and variance. This prompts the need for the employment of ensemble stacking generalization with artificial neural networks (ANN) to ensure an improved performance with accurate results. This approach combined four models namely random forest (RF), generalized boosted regression model (GBRM), Evolutional Algorithm (EvA), and artificial neural network (ANN). The inner mechanism of the stacked EvA-RF-GBRM-ANN model involves creating meta-data from EvA, RF, and GBRM models to calculate the final estimates using ANN. This work proposes a stacked neural network for short-term load forecasting through a view of dropping predicting faults besides their discrepancy associated with sole-based models and stacked neural networks (SNN).</em>
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Mahmud Iwan Solihin, Chan Jin Yuan, Wan Siu Hong, et al. "SPECTROSCOPY DATA CALIBRATION USING STACKED ENSEMBLE MACHINE LEARNING." IIUM Engineering Journal 25, no. 1 (2024): 208–24. http://dx.doi.org/10.31436/iiumej.v25i1.2796.
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Near infrared spectroscopy (NIRS) is a widely used analytical technique for non-destructive analysis of various materials including food fraud detection. However, the accurate calibration of NIRS data can be challenging due to the complexity of the underlying relationships between the spectral data and the target variables of interest. Ensemble learning, which combines multiple models to make predictions, has been shown to improve the accuracy and robustness of predictive models in various domains. This paper proposes stacking ensemble machine learning (SEML) for calibration of NIRS data with two levels of learning involved. Eight (8) spectroscopy datasets from public repository and previously published works by the authors are used as the case study. The model well generalized the data in the respective regression tasks with of at least »0.8 in the test samples and in the respective classification tasks with classification accuracy (CA) of at least »0.8 also. In addition, the proposed SEML can improve, or at least reach par with, the accuracy of individual base learners in both train and test samples for all cases of regression and classification datasets. It shows superior performance in test samples for both regression and classification datasets with respectively ranging from 0.86 to nearly 1 and CA ranging from 0.89 to 1. ABSTRAK: Spektroskopi inframerah dekat (NIRS) adalah teknik analitikal yang banyak digunakan bagi analisa pelbagai bahan tanpa merosakkan bahan termasuk ketika mengesan penipuan makanan. Walau bagaimanapun, kalibrasi yang tepat bagi data NIRS adalah sangat mencabar kerana hubungan antara data spektral dan pemboleh ubah sasaran yang ingin dikaji bersifat kompleks. Gabungan pembelajaran (Ensemble learning), iaitu gabungan pelbagai model bagi membuat prediksi, telah terbukti dapat meningkatkan ketepatan dan kecekapan model prediksi dalam pelbagai bentuk. Kajian ini mencadangkan Turutan Gabungan Pembelajaran Mesin (Stacking Ensemble Machine Learning ) (SEML), bagi teknik penentu ukuran data NIRS melibatkan dua tahap pembelajaran. Lapan (8) set data spektroskopi dari repositori awam dan kajian terdahulu oleh pengarang telah digunakan sebagai kes kajian. Model ini menggeneralisasi data dalam tugas regresi masing-masing sebanyak ?0.8 bagi sampel ujian dan pengelasan tugas masing-masing dengan ketepatan klasifikasi (CA) sekurang-kurangnya ?0.8. Tambahan, SEML yang dicadangkan ini dapat membantu, atau sekurang-kurangnya setanding dengan ketepatan individu dalam pembelajaran berkumpulan dalam kedua-dua sampel latihan dan ujian bagi semua kes set data regresi dan klasifikasi. Ia menunjukkan prestasi terbaik dalam sampel ujian bagi kedua-dua kumpulan set data regresi dan klasifikasi dengan masing-masing antara 0.86 hingga hampir 1 dan antara julat 0.89 hingga 1 bagi CA.
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Liu, Xiangjuan, Qiaonan Yang, Rurou Yang, Lin Liu, and Xibing Li. "Corn Yield Prediction Based on Dynamic Integrated Stacked Regression." Agriculture 14, no. 10 (2024): 1829. http://dx.doi.org/10.3390/agriculture14101829.
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This study focuses on the problem of corn yield prediction, and a novel prediction model based on a dynamic ensemble stacking regression algorithm is proposed. The model aims to achieve more accurate corn yield prediction based on the in-depth exploration of the potential correlations in multisource and multidimensional data. Data on the weather conditions, mechanization degree, and maize yield in Qiqihar City, Heilongjiang Province, from 1995 to 2022, are used. Important features are determined and extracted effectively by using principal component analysis and indicator contribution assessment methods. Based on the combination of an early stopping mechanism and parameter grid search optimization, the performance of eight base models, including a deep learning model, is fine-tuned. Based on the theory of heterogeneous ensemble learning, a threshold is established to stack the high-performing models, realizing a dynamic ensemble mechanism and employing averaging and optimized weighting methods for prediction. The results demonstrate that the prediction accuracy of the proposed dynamic ensemble regression model is significantly better as compared to the individual base models, with the mean squared error (MSE) being as low as 0.006, the root mean squared error (RMSE) being 0.077, the mean absolute error (MAE) being 0.061, and a high coefficient of determination value of 0.88. These findings not only validate the effectiveness of the proposed approach in the field of corn yield prediction but also highlight the positive role of multisource data fusion in enhancing the performance of prediction models.
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Hui, Yang, Xuesong Mei, Gedong Jiang, Tao Tao, Changyu Pei, and Ziwei Ma. "Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model." Shock and Vibration 2019 (November 3, 2019): 1–16. http://dx.doi.org/10.1155/2019/7386523.
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Milling tool wear state recognition plays an important role in controlling the quality of milled parts and reducing machine tool downtime. However, the characteristics of milling process limit the accuracy and stability of tool condition monitoring (TCM) employing vibration signals. To improve this problem, this paper explores the use of vibration signals as sensing approach for recognizing tool wear states during milling operation by using the stacked generalization (SG) ensemble model. In this study, vibration signals collected during the milling process are analyzed through the time domain, frequency domain, and time-frequency domain to extract signal features. The support vector machine recursive feature elimination (SVM-RFE) algorithm is used to select the main features which are most relevant to tool wear states. The SG ensemble model based on SVM, decision tree (DT), naive Bayes (NB), and SG ensemble strategy is constructed to recognize tool wear states. The proposed method is experimental verified, and the results show that the recognition accuracy of the established SG ensemble model is 98.74% and the overall G-mean and AUC evaluation value of the model is 0.98 and 0.98, respectively. In addition, compared with other ensemble models and single models, the SG ensemble model based on vibration signals has better recognition accuracy and stability than other models.
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An, Lifei. "Machine Learning-Based Stacked Ensemble Models for Predicting Heart Disease." Applied and Computational Engineering 155, no. 1 (2025): 168–73. https://doi.org/10.54254/2755-2721/2025.gl23424.
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Heart disease is still the leading cause of mortality worldwide. Hence, advanced prediction models are necessary for early detection and care. By combining three base classifiersK-Nearest Neighbors (KNN), Support Vector Machines (SVMs), and Random Forests (RFs)with logistic regression as a meta-model, this work suggests a stacked ensemble model to increase the accuracy of heart disease prediction. Specifically, this paper utilizes KNN to capture local data patterns. Secondly, SVM deals with high-dimensional feature spaces, and RF is utilized to mitigate overfitting through ensemble voting. Logistic regression is used to weight its predictions optimally. The model was trained and tested on the UCI Heart Disease dataset (303 records, 14 clinical features). This paper's preprocessing stage deals with missing values, normalizes numerical data, and codes categorical variables. To optimize the performance, the study also performs hyperparameter tuning and cross-validation. According to the experimental data, the stacked ensemble outperforms a single model (KNN: 85.2%; SVM: 88.6%; RF: 89.3%) with an accuracy of 93.7%. Key risk factors identified included maximum heart rate, cholesterol level, and type of chest pain. Consistency with clinical knowledge validated the reliability of the model. This study highlights how effective ensemble learning is at increasing diagnostic precision, and it shows promise for use in clinical decision support systems. Future work will explore further integrating genetic and lifestyle data and deployment in real-time healthcare environments to improve predictive robustness and patient outcomes.
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Li, Fuyuan, Zhanjin Wang, Ruiling Bian, et al. "Predicting the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis using a stacked ensemble machine learning model: a retrospective study based on the MIMIC database." BMJ Open 15, no. 2 (2025): e087427. https://doi.org/10.1136/bmjopen-2024-087427.
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ObjectiveThis study developed and validated a stacked ensemble machine learning model to predict the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis.DesignA retrospective study based on patient data from public databases.ParticipantsThis study analysed 1295 patients with acute pancreatitis complicated by septicaemia from the US Intensive Care Database.MethodsFrom the MIMIC database, data of patients with acute pancreatitis and sepsis were obtained to construct machine learning models, which were internally and externally validated. The Boruta algorithm was used to select variables. Then, eight machine learning algorithms were used to construct prediction models for acute kidney injury (AKI) occurrence in intensive care unit (ICU) patients. A new stacked ensemble model was developed using the Stacking ensemble method. Model evaluation was performed using area under the receiver operating characteristic curve (AUC), precision-recall (PR) curve, accuracy, recall and F1 score. The Shapley additive explanation (SHAP) method was used to explain the models.Main outcome measuresAKI in patients with acute pancreatitis complicated by sepsis.ResultsThe final study included 1295 patients with acute pancreatitis complicated by sepsis, among whom 893 cases (68.9%) developed acute kidney injury. We established eight base models, including Logit, SVM, CatBoost, RF, XGBoost, LightGBM, AdaBoost and MLP, as well as a stacked ensemble model called Multimodel. Among all models, Multimodel had an AUC value of 0.853 (95% CI: 0.792 to 0.896) in the internal validation dataset and 0.802 (95% CI: 0.732 to 0.861) in the external validation dataset. This model demonstrated the best predictive performance in terms of discrimination and clinical application.ConclusionThe stack ensemble model developed by us achieved AUC values of 0.853 and 0.802 in internal and external validation cohorts respectively and also demonstrated excellent performance in other metrics. It serves as a reliable tool for predicting AKI in patients with acute pancreatitis complicated by sepsis.
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Xu, Lu, Si-Min Yan, Chen-Bo Cai, et al. "Nonlinear Multivariate Calibration of Shelf Life of Preserved Eggs (Pidan) by Near Infrared Spectroscopy: Stacked Least Squares Support Vector Machine with Ensemble Preprocessing." Journal of Spectroscopy 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/797302.
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This paper aims at developing a rapid and nondestructive method for analyzing the shelf life of preserved eggs (pidan) by near infrared (NIR) spectroscopy and nonlinear multivariate calibration. A major concern with a nonlinear model is that the noncomposition-correlated spectral variations among pidan objects of different batches and production dates would unnecessarily increase model complexity and cause overfitting and degradation of prediction. To reduce the negative influence of unwanted spectral variations, stacked least squares support vector machine (LS-SVM) with an ensemble of 62 commonly used preprocessing methods is proposed to automatically optimize data preprocessing and develop the nonlinear model. The analysis results indicate that stacked LS-SVM can obtain stable calibration model, and the prediction accuracy is improved compared with models with single-preprocessing methods. Since LS-SVM is much faster than its ordinary counterparts, stacked LS-SVM with ensemble preprocessing can be performed within an acceptable computational time. When the objects and spectral variations are very complex, the proposed method can provide a useful tool for data preprocessing and nonlinear multivariate calibration.
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Karen, Charly Veigas, Srilekha Regulagadda Durga, and Arun Kokatnoor Sujatha. "Optimized Stacking Ensemble (OSE) for Credit Card Fraud Detection using Synthetic Minority Oversampling Model." Indian Journal of Science and Technology 14, no. 32 (2021): 2607–15. https://doi.org/10.17485/IJST/v14i32.807.
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Abstract <strong>Objectives:</strong> Credit fraud is a global threat to financial institutions due to specific challenges like imbalanced datasets and hidden patterns in real-life scenarios. The objective of this study is to propose a model that effectively identifies fraudulent transactions. <strong>Methods:</strong> Methods such as Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Networks (GAN) that artificially generate synthetic data are used in this paper to approximate the distribution of data among the two classes in the original dataset. After balancing the dataset, the individual models Multi-Layer Perceptron (MLP), k- Nearest Neighbors algorithm (kNN) and Support Vector Machine (SVM) are trained on the augmented dataset to establish an initial improvement at the data level. These base-classifiers are further incorporated into the Optimized Stacked Ensemble (OSE) learning process to fit the meta-classifier which creates an effective predictive model for fraud detection. All base-classifiers and the final Optimized Stacked Ensemble (OSE) have been implemented to critically assess and evaluate their performances.<strong> Findings:</strong> Empirical results obtained in this paper show that the quality of the final dataset is considerably improved when Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Networks (GAN) are used as oversampling algorithms. The Multi-Layer Perceptron model showed an increase of 10% in the F1 Score while kNN and SVM showed an increase of 3% each. The optimized model is built using a Stacking Classifier that combines the GAN-improved Multi-Perceptron Model with the other standard classification models such as KNN and SVM. This ensemble outperforms the existing enhanced Multi-Layer Perceptron with near-perfect accuracy (99.86%) and an increase of 16% in F1 Score, resulting in an effective fraud detection mechanism. <strong>Novelty:</strong> For the current dataset, the Optimized Stacked Ensemble model shows an increase of 16% in F1 Score as compared to the existing Multi-Perceptron model. <strong>Keywords:</strong> Ensemble; Credit Card; Fraud Detection; GAN; SMOTE; MLP
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Chan, Hong Ru. "Rent Price Prediction with Advanced Machine Learning Methods: A Comparison of California and Texas." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 501–10. http://dx.doi.org/10.54097/84vvv580.
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The forecast of rent prices in dynamic housing markets is of fundamental importance to renters, landlords, investors, and politicians alike. Machine learning models offer flexibility, excel at modeling complex relationships, and provide outstanding forecast precision. This study compares advanced machine learning models, extreme gradient boosting regressor (XGBoost), light gradient boosting machine (LightGBM), random forest, ridge regression, and two ensemble approaches, to predict California and Texas rent prices. The two ensemble approaches include a hybrid regression of averaging base models and a 2-level stacked generalization model. The results revealed that a stacked generalization ensemble with base models random forest, XGBoost, LightGBM, and meta-model ridge regression achieved the best performance for the California dataset by generating the lowest MSE and highest R2 of 46116.3 and 0.8858, respectively. In contrast, random forest outperformed both ensemble models with the lowest MSE and MAPE of 18401.93 and 9.7003%, respectively, and the highest R2 of 0.7992. These methodologies can assess future rental property worth, serve as indicators for market dynamics, and aid in establishing real estate policies, thereby providing practical guidance to individuals, businesses, and policymakers.
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Rahim, Md Abdur, Md Alfaz Hossain, Md Najmul Hossain, Jungpil Shin, and Keun Soo Yun. "Stacked Ensemble-Based Type-2 Diabetes Prediction Using Machine Learning Techniques." Annals of Emerging Technologies in Computing 7, no. 1 (2023): 30–39. http://dx.doi.org/10.33166/aetic.2023.01.003.
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Diabetes is a long-term disease caused by the human body's inability to make enough insulin or to use it properly. This is one of the curses of the present world. Although it is not very severe in the initial stage, over time, it takes a deadly shape and gradually affects a variety of human organs, such as the heart, kidney, liver, eyes, and brain, leading to death. Many researchers focus on the machine and in-depth learning strategies to efficiently predict diabetes based on numerous risk variables such as insulin, BMI, and glucose in this healthcare issue. We proposed a robust approach based on the stacked ensemble method for predicting diabetes using several machine learning (ML) methods. The stacked ensemble comprises two models: the base model and the meta-model. Base models use a variety of models of ML, such as Support Vector Machine (SVM), K Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF), which make different assumptions about predictions, and meta-models make final predictions using Logistic Regression from predictive outputs from base models. To assess the efficiency of the proposed model, we have considered the PIMA Indian Diabetes Dataset (PIMA-IDD). We used linear and stratified sampling to ensure dataset consistency and K-fold cross-validation to prevent model overfitting. Experiments revealed that the proposed stacked ensemble model outperformed the model specified in the base classifier as well as the comprehensive methods, with an accuracy of 94.17%.
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Rodwan, Elhashmi, P. Hallinan Kevin, and Alanezi Abdulrahman. "Roadmap for Utilizing Machine Learning in Building Energy Systems Applications: Case Study of Predicting Chiller Running Capacity for School Buildings Using Stacking Learning." Journal of Energy & Technology (JET) 1, no. 1 (2021): 35–45. https://doi.org/10.5281/zenodo.4560626.
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Cooling accounts for 12-38% of total energy consumption in schools in the US, depending on the region. In this study, stacking learning is utilized to predict chiller running capacity for four school buildings (regression) and to predict the chiller status for four another schools (classification) using a collection of interval chiller data and building demand. Singular and multiple measurement periods within one or more seasons are considered. A generalized methodology for modeling building energy systems is posited that informs selection of features, data balancing to attain the best model possible, ensemble-based stacked learning in order to prevent over-fitting, and final model development based upon the results from the stacked learning. The results show that ensemble-based stacked learning improves the model performance substantially; providing the most accurate results for both regression and classification. for both classification and regression. For, classification, the balanced accuracy is 99.79% while Kappa is 99.39%. For regression, the R-squared value, the mean absolute error (MAE) error, and the root mean squared error (RMSE) are 1.78 kW, 2.77 kW, and 0.983 respectively.
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Hornbostel, Scott C. "Seismic system analysis—A case study from the Gulf of Mexico." GEOPHYSICS 63, no. 5 (1998): 1618–28. http://dx.doi.org/10.1190/1.1444458.
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It is often difficult to predict the effects of change in seismic acquisition or processing on data quality. To address this difficulty, ensemble averaging can be used to estimate the final stacked‐trace effects of altering aspects of the seismic acquisition or processing. This method of studying the seismic system begins by modeling of an ensemble of data gathers based on some geological region of interest. The model gathers are subsequently processed to create stacked traces that are compared with related reference traces to make signal‐to‐noise ratio (SNR) or bandwidth measurements. The ensemble average of these stacked‐trace measurements can then be examined while some aspect of the simulated acquisition or processing is adjusted. The result of this analysis is a plot illustrating the sensitivity of the seismic system (i.e., the stacked‐trace quality) to the selected parameter under investigation. This ensemble averaging approach was used to study system sensitivities for a data set collected in the Gulf of Mexico. The specific issues examined in this analysis include source and receiver parameters, ambient noise levels, spatial sampling, velocity picking, velocity errors, and stretch muting. Of the parameters studied, the final system output was most sensitive to velocity errors. Even differences of 1–2% in the stacking velocity led to noticeable degradation of the stacked‐trace bandwidth and SNR. This velocity sensitivity was evident in both the model and field data. Certain parameters, such as gun volumes, were less important for typical values. The ambient noise level and spatial sampling effects were similarly less important except in the deeper portions of the data (where the unstacked SNR was fairly low). These insensitivities are interesting because they imply potential cost savings. The percent stretch‐mute study was interesting because SNR and bandwidth were optimized with different mutes. All study results by design, are, tied to a specific data area. Nonetheless, these findings may provide an initial direction for system studies in other areas.
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Al-Karawi, Ahmed, and Ercan Avşar. "Stacked Cross Validation with Deep Features." Tehnički glasnik 16, no. 1 (2022): 33–39. http://dx.doi.org/10.31803//tg-20210422205610.
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Detection of malignant skin lesions is important for early and accurate diagnosis of skin cancer. In this work, a hybrid method for malignant lesion detection from dermoscopy images is proposed. The method combines the feature extraction process of convolutional neural networks (CNN) with an ensemble learner called stacked cross-validation (CV). The features extracted by three different CNN architectures, namely, ResNet50, Xception, and VGG16 are used for training of four different baseline classifiers, which are support vector machines, k-nearest neighbors, artificial neural networks, and random forests. The stacked outputs of these classifiers are used to train a logistic regression model as a meta-classifier. The performance of the proposed method is compared with the baseline classifiers trained individually as well as AdaBoost classifier, another ensemble learner. Feature extraction with Xception architecture, outperforms all other benchmark models by achieving scores of 0.909, 0.896, 0.886, and 0.917 for accuracy, F1-score, sensitivity, and AUC, respectively.
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Qi, Fei, and Hangyu Li. "A Two-Level Machine Learning Prediction Approach for RAC Compressive Strength." Buildings 14, no. 9 (2024): 2885. http://dx.doi.org/10.3390/buildings14092885.
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Through the use of recycled aggregates, the construction industry can mitigate its environmental impact. A key consideration for concrete structural engineers when designing and constructing concrete structures is compressive strength. This study aims to accurately forecast the compressive strength of recycled aggregate concrete (RAC) using machine learning techniques. We propose a simplified approach that incorporates a two-layer stacked ensemble learning model to predict RAC compressive strength. In this framework, the first layer consists of ensemble models acting as base learners, while the second layer utilizes a random forest (RF) model as the meta-learner. A comparative analysis with four other ensemble learning models demonstrates the superior performance of the proposed stacked model in effectively integrating predictions from the base learners, resulting in enhanced model accuracy. The model achieves a low mean absolute error (MAE) of 2.599 MPa, a root mean squared error (RMSE) of 3.645 MPa, and a high R-squared (R2) value of 0.964. Additionally, a Shapley (SHAP) additive explanation analysis reveals the influence and interrelationships of various input factors on the compressive strength of RAC, aiding design and construction professionals in optimizing raw material content during the RAC design and production process.
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Paul, Arunya, Tejaswini Kar, Sasmita Pahadsingh, Priya Chandan Satpathy, and Biswaranjan Behera. "Performance Comparison of different Disease Detection using Stacked Ensemble Learning Model." March 2024 6, no. 1 (2024): 26–39. http://dx.doi.org/10.36548/jscp.2024.1.003.
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Malignancy risks and genetic disorders have long been challenging due to procedures that lack precision and predictability, thereby complicating the precise identification of diseases and their root causes. Machine learning classifiers have emerged as more suitable and effective tools. Various machine learning classifiers have been utilized to examine different genetic disorders, and the results from these classifiers have been further compared to determine their superiority. In this study, a variety of classifiers, including the SVM, KNN, decision tree, random forest, and logistic regression algorithms, are examined. These classifiers utilize specific training variables to analyze how input values correspond to the respective class. After successfully implementing each classifier, we proceeded to employ Stacking, an ensemble machine learning technique that aggregates predictions from individual classifiers on the same dataset. Four datasets, including the breast cancer, diabetes, Parkinson’s, and genomic datasets, were successfully implemented using the aforementioned methods, and the results obtained showed how the input values correspond to the class using a few training variables. SVM classifier was shown to be the most effective of the five described classifiers, having the highest accuracy in most of the cases. It provided accuracies of 97.43%, 97.46%, 97.45%, and 97.44% for each of the genome cancer, diabetes, Parkinson’s, and breast cancer datasets. The KNN and Random Forest models also came out to be very effective, with accuracy around 95% and 91%, respectively, for various disease datasets. The Logistic Regression and Decision Tree models also worked well. However, the ensemble method of Stacking proved to be highly efficient above all other base models and generated accuracies above 97.5% for all the aforementioned diseases.
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Abbas, Sidra, Gabriel Avelino Sampedro, Shtwai Alsubai, Ahmad Almadhor, and Tai-hoon Kim. "An Efficient Stacked Ensemble Model for Heart Disease Detection and Classification." Computers, Materials & Continua 77, no. 1 (2023): 665–80. http://dx.doi.org/10.32604/cmc.2023.041031.
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Tama, Bayu Adhi, Sun Im, and Seungchul Lee. "Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble." BioMed Research International 2020 (April 27, 2020): 1–10. http://dx.doi.org/10.1155/2020/9816142.
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Coronary heart disease (CHD) is one of the severe health issues and is one of the most common types of heart diseases. It is the most frequent cause of mortality across the globe due to the lack of a healthy lifestyle. Owing to the fact that a heart attack occurs without any apparent symptoms, an intelligent detection method is inescapable. In this article, a new CHD detection method based on a machine learning technique, e.g., classifier ensembles, is dealt with. A two-tier ensemble is built, where some ensemble classifiers are exploited as base classifiers of another ensemble. A stacked architecture is designed to blend the class label prediction of three ensemble learners, i.e., random forest, gradient boosting machine, and extreme gradient boosting. The detection model is evaluated on multiple heart disease datasets, i.e., Z-Alizadeh Sani, Statlog, Cleveland, and Hungarian, corroborating the generalisability of the proposed model. A particle swarm optimization-based feature selection is carried out to choose the most significant feature set for each dataset. Finally, a two-fold statistical test is adopted to justify the hypothesis, demonstrating that the performance differences of classifiers do not rely upon an assumption. Our proposed method outperforms any base classifiers in the ensemble with respect to 10-fold cross validation. Our detection model has performed better than current existing models based on traditional classifier ensembles and individual classifiers in terms of accuracy, F1, and AUC. This study demonstrates that our proposed model adds a considerable contribution compared to the prior published studies in the current literature.
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Ornstein, Joseph T. "Stacked Regression and Poststratification." Political Analysis 28, no. 2 (2019): 293–301. http://dx.doi.org/10.1017/pan.2019.43.
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I develop a procedure for estimating local-area public opinion called stacked regression and poststratification (SRP), a generalization of classical multilevel regression and poststratification (MRP). This procedure employs a diverse ensemble of predictive models—including multilevel regression, LASSO, k-nearest neighbors, random forest, and gradient boosting—to improve the cross-validated fit of the first-stage predictions. In a Monte Carlo simulation, SRP significantly outperforms MRP when there are deep interactions in the data generating process, without requiring the researcher to specify a complex parametric model in advance. In an empirical application, I show that SRP produces superior local public opinion estimates on a broad range of issue areas, particularly when trained on large datasets.
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Kim, Youngjun, and Stéphane M. Meystre. "Ensemble method–based extraction of medication and related information from clinical texts." Journal of the American Medical Informatics Association 27, no. 1 (2019): 31–38. http://dx.doi.org/10.1093/jamia/ocz100.
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Abstract Objective Accurate and complete information about medications and related information is crucial for effective clinical decision support and precise health care. Recognition and reduction of adverse drug events is also central to effective patient care. The goal of this research is the development of a natural language processing (NLP) system to automatically extract medication and adverse drug event information from electronic health records. This effort was part of the 2018 n2c2 shared task on adverse drug events and medication extraction. Materials and Methods The new NLP system implements a stacked generalization based on a search-based structured prediction algorithm for concept extraction. We trained 4 sequential classifiers using a variety of structured learning algorithms. To enhance accuracy, we created a stacked ensemble consisting of these concept extraction models trained on the shared task training data. We implemented a support vector machine model to identify related concepts. Results Experiments with the official test set showed that our stacked ensemble achieved an F1 score of 92.66%. The relation extraction model with given concepts reached a 93.59% F1 score. Our end-to-end system yielded overall micro-averaged recall, precision, and F1 score of 92.52%, 81.88% and 86.88%, respectively. Our NLP system for adverse drug events and medication extraction ranked within the top 5 of teams participating in the challenge. Conclusion This study demonstrated that a stacked ensemble with a search-based structured prediction algorithm achieved good performance by effectively integrating the output of individual classifiers and could provide a valid solution for other clinical concept extraction tasks.
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Mahendran. S. "Diagnostic Predictive Approaches for Liver Disease Detection using Stacked Ensemble Model with Data Augmentation." Journal of Information Systems Engineering and Management 10, no. 13s (2025): 750–60. https://doi.org/10.52783/jisem.v10i13s.2157.
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The global medical fraternity is challenged with evolving a perfect prediction model that diagnoses a liver ailment at the right time and calls for an immediate medical intervention for its critical need. Potential threat to life of liver disease chronically contracted stresses for necessity for its root cause and time-bound medical remedy. By making use of a dataset of Indian liver patients from the pool of data maintained at the national level, this work is entailed with the introduction of an architecture of innovative nature that desegregates Stacked Ensemble Model, feature engineering for foretelling liver ailments. The core contribution to this work is the harness of feature engineering utilizing SHapely Additive exPlanations (SHAP), encompassing the state-of-the-art techniques that are not delved into by data-driven machine learning approaches that are in vogue now as a vaticinator of liver ailment. The labyrinthine design of Stacked Ensemble Model facilitates timely prediction of liver disease, exploiting the maximum of learning techniques that ramp up the detection activity and augment accurate diagnosing. The methodology exploits the potencies of varied base learners like Logistic Regression, Multi-layer Perceptron, Support Vector Machine, Decision Tree, K-nearest neighbor and Extra trees classifier. These diverse views are homogenized as input to the meta-learner, the Random Forest, to build a sturdy and trustworthy predictive model. The commissioning of Stacked Ensemble Model with feature engineering produced an accuracy of a cent percent short of 3. This methodology is aimed at fine tuning the prediction of liver ailments, ensuring smooth, effective and timely intervention, and assuring of an effective management.
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Qayyum, Hafza, Syed Tahir Hussain Rizvi, Muddasar Naeem, Umamah bint Khalid, Musarat Abbas, and Antonio Coronato. "Enhancing Diagnostic Accuracy for Skin Cancer and COVID-19 Detection: A Comparative Study Using a Stacked Ensemble Method." Technologies 12, no. 9 (2024): 142. http://dx.doi.org/10.3390/technologies12090142.
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In recent years, COVID-19 and skin cancer have become two prevalent illnesses with severe consequences if untreated. This research represents a significant step toward leveraging machine learning (ML) and ensemble techniques to improve the accuracy and efficiency of medical image diagnosis for critical diseases such as COVID-19 (grayscale images) and skin cancer (RGB images). In this paper, a stacked ensemble learning approach is proposed to enhance the precision and effectiveness of diagnosis of both COVID-19 and skin cancer. The proposed method combines pretrained models of convolutional neural networks (CNNs) including ResNet101, DenseNet121, and VGG16 for feature extraction of grayscale (COVID-19) and RGB (skin cancer) images. The performance of the model is evaluated using both individual CNNs and a combination of feature vectors generated from ResNet101, DenseNet121, and VGG16 architectures. The feature vectors obtained through transfer learning are then fed into base-learner models consisting of five different ML algorithms. In the final step, the predictions from the base-learner models, the ensemble validation dataset, and the feature vectors extracted from neural networks are assembled and applied as input for the meta-learner model to obtain final predictions. The performance metrics of the stacked ensemble model show high accuracy for COVID-19 diagnosis and intermediate accuracy for skin cancer.
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Zhang, Xuqun, Zhili Li, Yaohua Sui, Chengjun Liu, and Zhaofeng Li. "Hybrid soil strength prediction model for geotechnical ground investigation using convolutional neural network and ensemble learning." Journal of Physics: Conference Series 2816, no. 1 (2024): 012066. http://dx.doi.org/10.1088/1742-6596/2816/1/012066.
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Abstract A hybrid model of soil strength prediction from the image data and feature data of soil was proposed, which is aimed at providing an efficient solution in geotechnical ground investigation by leveraging the power of AI. In this model, CNN purely with the encoder was used to establish the relationship between image data and strength parameters of soil, while ensemble learning stacked of three base learners, i.e., KNN, LASSO, and LSVM, was applied to the feature data. Then, the CNN part and the ensemble learning part were integrated into the loss function. Results show that the convergence of the hybrid model was slower than that of the ensemble learner. However, with the aid of soil image which has more features of soil in essence, the predicted soil strengths by the hybrid model better matched with the actual ones, compared with the ensemble learner and base learners. This hybrid model offers an effective framework of multi-modal data fusion for geotechnical engineering, by leveraging the synergy of the high-dimensional feature extraction capabilities of CNN and the generalization abilities of ensemble learning.