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Journal articles on the topic 'Ensemble learning model'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Wang, Wenjia, and Yi-Hui Zhou. "A Double Penalty Model for Ensemble Learning." Mathematics 10, no. 23 (2022): 4532. http://dx.doi.org/10.3390/math10234532.

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Modern statistical learning techniques often include learning ensembles, for which the combination of multiple separate prediction procedures (ensemble components) can improve prediction accuracy. Although ensemble approaches are widely used, work remains to improve our understanding of the theoretical underpinnings of aspects such as identifiability and relative convergence rates of the ensemble components. By considering ensemble learning for two learning ensemble components as a double penalty model, we provide a framework to better understand the relative convergence and identifiability of

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Deshmukh, Pratiksha, and Harshali Patil. "Depression Prediction Model based on Ensemble Learning Classifier." Indian Journal Of Science And Technology 17, no. 39 (2024): 4084–93. http://dx.doi.org/10.17485/ijst/v17i39.159.

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Objective: The main objective of this research was to develop a suitable prediction model to classify the symptoms of depression experienced by people. Methodology: This research incorporates the dataset of the “Centres for Disease Control and Prevention National Health and Nutrition Examination Survey,” which was available on GitHub. After that, pre-processing of the dataset was done using the infinite latent feature selection (ILFS) algorithm to extract the appropriate features from the dataset. After that, the dataset was split into 70:30 ratios. About 70% of the data is employed for traini

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Theresa, S. Josephine. "Weighted Model Fusion for Imbalanced Learning." Indian Journal Of Science And Technology 18, no. 23 (2025): 1818–24. https://doi.org/10.17485/ijst/v18i23.904.

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Objectives: To develop a Weighted Multi-model Ensemble (WME) to improve binary and multiclass data predictions, particularly in handling imbalanced datasets. The model aims to achieve high performance metrics, such as precision and recall, while minimizing false positive rates. Additionally, the study seeks to explore better handling mechanisms to enhance prediction accuracy further. Methods: The methodology involves a two-phase approach for the Weighted Multi-Model Ensemble (WME). The first phase includes data preprocessing, segregating training and test data, and training models like Decisio

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Patel, Mamta, and Mehul Shah. "Deep Ensemble Learning Model for Diagnosis of Lung Diseases from Chest X -Ray Images." Indian Journal Of Science And Technology 17, no. 8 (2024): 702–12. http://dx.doi.org/10.17485/ijst/v17i8.3151.

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Objectives: This study aims to develop a robust medical recognition system using deep learning for the identification of various lung diseases, including COVID-19, pneumonia, lung opacity, and normal states, from chest X-ray images. The focus is on implementing ensemble fixed features learning methods to enhance diagnostic capabilities, contributing to the development of a cost-effective and reliable diagnostic tool for combating the global epidemic of lung disorders. Methods: The study utilizes a Kaggle dataset containing COVID-19 chest radiography images. Raw X-ray images undergo preprocessi

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j, j., Jin Gwang Koh, and Sung Keun Lee. "Harvest Forecasting Improvement Using Federated Learning and Ensemble Model." Korean Institute of Smart Media 12, no. 10 (2023): 9–18. http://dx.doi.org/10.30693/smj.2023.12.10.9.

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Harvest forecasting is the great demand of multiple aspects like temperature, rain, environment, and their relations. The existing study investigates the climate conditions and aids the cultivators to know the harvest yields before planting in farms. The proposed study uses federated learning. In addition, the additional widespread techniques such as bagging classifier, extra tees classifier, linear discriminant analysis classifier, quadratic discriminant analysis classifier, stochastic gradient boosting classifier, blending models, random forest regressor, and AdaBoost are utilized together.

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Chang-You Zhang, Chang-You Zhang, Jing-Jing Wang Chang-You Zhang, Li-Xia Wan Jing-Jing Wang, and Ruo-Xue Yu Li-Xia Wan. "An Emotional Analysis Method Based on Multi Model Ensemble Learning." 電腦學刊 34, no. 1 (2023): 001–11. http://dx.doi.org/10.53106/199115992023023401001.

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<p>Traditional machine learning models generally use weak supervision model, which is difficult to adapt to the scene of multi classification for emotional text. Therefore, a multi model ensemble learning algorithm for emotional text classification is proposed. The algorithm takes the labeled emotional text data as the training sample, uses the improved TF-IDF algorithm to train the word vector space model, selects three weakly supervised machine learning algorithms, linear SVC, xgboost and logistic regression, to construct the base classifier, and uses the random forest algorithm to con

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d, d., d. d, d. d, and d. d. "Optimized Deep Learning Models Using Ensemble Learning for COVID-19 Detection on CT Scan Images." Korean Data Analysis Society 25, no. 6 (2023): 2027–39. http://dx.doi.org/10.37727/jkdas.2023.25.6.2027.

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Early identification of COVID-19 can facilitate the establishment of a swift medical response plan, thereby slowing the rapid dissemination of this deadly disease. Recent advancements in medical imaging technology, coupled with the successful application of deep learning to visual tasks, have driven numerous studies investigating early disease diagnosis through medical imaging. In particular, deep learning has been employed for COVID-19 diagnosis from CT scan images. This paper proposes an ensemble COVID detection model that integrates four models including GoogleNet, EfficientNet, Hybrid Effi

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Siswoyo, Bambang, Zuraida Abal Abas, Ahmad Naim Che Pee, Rita Komalasari, and Nano Suryana. "Ensemble machine learning algorithm optimization of bankruptcy prediction of bank." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 679. http://dx.doi.org/10.11591/ijai.v11.i2.pp679-686.

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The ensemble consists of a single set of individually trained models, the predictions of which are combined when classifying new cases, in building a good classification model requires the diversity of a single model. The algorithm, logistic regression, support vector machine, random forest, and neural network are single models as alternative sources of diversity information. Previous research has shown that ensembles are more accurate than single models. Single model and modified ensemble bagging model are some of the techniques we will study in this paper. We experimented with the banking in

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Bambang, Siswoyo, Abal Abas Zuraida, Naim Che Pee Ahmad, Komalasari Rita, and Suyatna Nano. "Ensemble machine learning algorithm optimization of bankruptcy prediction of bank." International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 679–86. https://doi.org/10.11591/ijai.v11.i2.pp679-686.

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The ensemble consists of a single set of individually trained models, the predictions of which are combined when classifying new cases, in building a good classification model requires the diversity of a single model. The algorithm, logistic regression, support vector machine, random forest, and neural network are single models as alternative sources of diversity information. Previous research has shown that ensembles are more accurate than single models. Single model and modified ensemble bagging model are some of the techniques we will study in this paper. We experimented with the banking in

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Pahno, Steve, Jidong J. Yang, and S. Sonny Kim. "Use of Machine Learning Algorithms to Predict Subgrade Resilient Modulus." Infrastructures 6, no. 6 (2021): 78. http://dx.doi.org/10.3390/infrastructures6060078.

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Modern machine learning methods, such as tree ensembles, have recently become extremely popular due to their versatility and scalability in handling heterogeneous data and have been successfully applied across a wide range of domains. In this study, two widely applied tree ensemble methods, i.e., random forest (parallel ensemble) and gradient boosting (sequential ensemble), were investigated to predict resilient modulus, using routinely collected soil properties. Laboratory test data on sandy soils from nine borrow pits in Georgia were used for model training and testing. For comparison purpos

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Shen, Zhiqiang, Zhankui He, and Xiangyang Xue. "MEAL: Multi-Model Ensemble via Adversarial Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4886–93. http://dx.doi.org/10.1609/aaai.v33i01.33014886.

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Often the best performing deep neural models are ensembles of multiple base-level networks. Unfortunately, the space required to store these many networks, and the time required to execute them at test-time, prohibits their use in applications where test sets are large (e.g., ImageNet). In this paper, we present a method for compressing large, complex trained ensembles into a single network, where knowledge from a variety of trained deep neural networks (DNNs) is distilled and transferred to a single DNN. In order to distill diverse knowledge from different trained (teacher) models, we propose

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Mamta, Patel, and Shah Mehul. "Deep Ensemble Learning Model for Diagnosis of Lung Diseases from Chest X -Ray Images." Indian Journal of Science and Technology 17, no. 8 (2024): 702–12. https://doi.org/10.17485/IJST/v17i8.3151.

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Abstract <strong>Objectives:</strong> This study aims to develop a robust medical recognition system using deep learning for the identification of various lung diseases, including COVID-19, pneumonia, lung opacity, and normal states, from chest X-ray images. The focus is on implementing ensemble fixed features learning methods to enhance diagnostic capabilities, contributing to the development of a cost-effective and reliable diagnostic tool for combating the global epidemic of lung disorders. <strong>Methods:</strong> The study utilizes a Kaggle dataset containing COVID-19 ches

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Hartono, Hartono, Opim Salim Sitompul, Tulus Tulus, Erna Budhiarti Nababan, and Darmawan Napitupulu. "Hybrid Approach Redefinition (HAR) model for optimizing hybrid ensembles in handling class imbalance: a review and research framework." MATEC Web of Conferences 197 (2018): 03003. http://dx.doi.org/10.1051/matecconf/201819703003.

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The purpose of this research is to develop a research framework to optimize the results of hybrid ensembles in handling class imbalance issues. The imbalance class is a state in which the classification results give the number of instances in a class much larger than the number of instances in the other class. In machine learning, this problem can reduce the prediction accuracy and also reduce the quality of the resulting decisions. One of the most popular methods of dealing with class imbalance is the method of ensemble learning. Hybrid Ensembles is an ensemble learning method approach that c

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P, Kalaivani, and Selvi S. "Machine Learning Approach to Analyse Ensemble Models and Neural Network Model for E-Commerce Application." Indian Journal of Science and Technology 13, no. 28 (2020): 2849–57. https://doi.org/10.17485/IJST/v13i28.927.

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Abstract <strong>Objectives:</strong> The main objective of this study is to compare the performance evaluation of ensemble based methods and neural network learning on various combinations of unigram, bigram, and trigram feature vector along with feature selection (IG) and feature reduction (PCA) for sentiment classification of movie reviews. <strong>Methods:</strong> Bagging and Adaboost are the techniques used in ensemble learning to learn the sentiment classifier to get better classification accuracy, using SVM, NB as a core learner for different models of attribute vectors.

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Bensouda, Nissrine, Fkihi Sanaa El, and Rdouan Faizi. "A novel ensemble model for detecting fake news." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 1160–71. https://doi.org/10.11591/ijai.v13.i1.pp1160-1171.

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Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learni

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Nai-Arun, Nongyao, and Punnee Sittidech. "Ensemble Learning Model for Diabetes Classification." Advanced Materials Research 931-932 (May 2014): 1427–31. http://dx.doi.org/10.4028/www.scientific.net/amr.931-932.1427.

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This paper proposed data mining techniques to improve efficiency and reliability in diabetes classification. The real data set collected from Sawanpracharak Regional Hospital, Thailand, was fist analyzed by using gain-ratio feature selection techniques. Three well known algorithms; naïve bayes, k-nearest neighbors and decision tree, were used to construct classification models on the selected features. Then, the popular ensemble learning; bagging and boosting were applied using the three base classifiers. The results revealed that the best model with the highest accuracy was bagging with base

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B M, Rakshitha. "Ensemble Learning Frameworks in Cardiovascular Prognostics: Advancements in Predictive Analytics." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 2048–58. https://doi.org/10.22214/ijraset.2025.72558.

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Cardiovasculardisease remains a pervasive and serious global health concern, underscoring the necessity of accurate and timely risk assessment. Within the field of machine learning, ensemble methods have gained significant traction for their ability to predict cardiovascular outcomes. Established algorithms—such as Support Vector Machines, Random Forests, and Gradient Boosting—continue to serve as reliable mainstays. Recently, however, advanced ensemble approaches like stacking and CatBoost have garnered increased attention. Emerging research suggests these newer methodologies may, in some ins

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PETERSON, ADAM H., and TONY R. MARTINEZ. "REDUCING DECISION TREE ENSEMBLE SIZE USING PARALLEL DECISION DAGS." International Journal on Artificial Intelligence Tools 18, no. 04 (2009): 613–20. http://dx.doi.org/10.1142/s0218213009000305.

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This research presents a new learning model, the Parallel Decision DAG (PDDAG), and shows how to use it to represent an ensemble of decision trees while using significantly less storage. Ensembles such as Bagging and Boosting have a high probability of encoding redundant data structures, and PDDAGs provide a way to remove this redundancy in decision tree based ensembles. When trained by encoding an ensemble, the new model behaves similar to the original ensemble, and can be made to perform identically to it. The reduced storage requirements allow an ensemble approach to be used in cases where

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Bashir, Shazab, Arfan Jaffar, Muhammad Rashid, Sheeraz Akram, and Sohail Masood Bhatti. "Intelligent recognition of human activities using deep learning techniques." PLOS One 20, no. 4 (2025): e0321754. https://doi.org/10.1371/journal.pone.0321754.

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Recognition of Human Actions (HAR) Portrays a crucial significance in various applications due to its ability for analyzing behaviour of humans within videos. This research investigates HAR in Red, Green, and Blue, or RGB videos using frameworks for deep learning. The model’s ensemble method integrates the forecasts from two models, 3D-AlexNet-RF and InceptionV3 Google-Net, to improve accuracy in recognizing human activities. Each model independently predicts the activity, and the ensembles method merges these predictions, often using voting or averaging, to produce a more accurate and reliabl

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Aelgani, Vivekanand, and Dhanalaxmi Vadlakonda. "Explainable Artificial Intelligence based Ensemble Machine Learning for Ovarian Cancer Stratification using Electronic Health Records." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (2023): 78–84. http://dx.doi.org/10.17762/ijritcc.v11i7.7832.

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The purpose of this study is to show how ensemble learning-driven machine learning algorithms outperform individual machine learning algorithms at predicting ovarian cancer on a biomarker dataset. Additionally, this study provides model explanations using explainable Artificial Intelligence methods, The method involved gathering and combining 49 risk factors from 349 patients. We hypothesize that ensemble machine learning systems are superior to individual Machine Learning systems in predicting ovarian cancer. The Machine Learning system consists of five individual Machine Learning and five en

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Suresh, Subramanian, and Angeline Christobel Y. "A Hybrid Machine Learning Model to Predict Heart Disease Accurately." Indian Journal of Science and Technology 15, no. 12 (2022): 527–34. https://doi.org/10.17485/IJST/v15i12.104.

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Abstract <strong>Objective:</strong> To propose the most effective machine learning algorithm for predicting cardiac problems. <strong>Methods:</strong> The dataset used for this study is “heart” which was taken from www.kaggle.com. The heart dataset contains 13 features and a target variable. It is divided into 70 percent training set and 30 percent testing set. K-Fold cross-validation is used in this study for model evaluation and model selection. The K value chosen is ten. A Hybrid Ensemble machine learning model is built using a heterogeneous collection of weak l

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Rachman, Dian Arif, and Muhamad Akrom. "Ensemble Learning Model in Predicting Corrosion Inhibition Capability of Pyridazine Compounds." Journal of Multiscale Materials Informatics 1, no. 1 (2024): 38–43. http://dx.doi.org/10.62411/jimat.v1i1.10502.

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Empirical studies of possible compound corrosion inhibitors require a lot of money, time, and resources. Therefore, we used a machine learning (ML) paradigm based on quantitative structure-property relationship (QSPR) models to evaluate ensemble algorithms as predictors of corrosion inhibition efficiency (CIE) values. Our investigation reveals that the gradient boosting (GB) regressor model outperforms other ensemble-based models. This advantage is evaluated objectively using the metrics root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). In summary

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Muhlestein, Whitney, and Lola Blackwell Chambless. "386 An Externally Validated Machine Learning Ensemble Model Accurately Predicts Important Neurosurgical Outcomes." Neurosurgery 64, CN_suppl_1 (2017): 292. http://dx.doi.org/10.1093/neuros/nyx417.386.

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Abstract INTRODUCTION Machine learning (ML) uses sophisticated computer algorithms to “learn” patterns in complex, heterogeneous datasets, and then to apply those patterns to never-before-seen data. In this proof-of-concept study, we use a novel approach of algorithm selection and combination to build and externally validate a ML-based model that predicts extended hospitalization (>7 days) in patients undergoing craniotomy for brain tumor, an important outcome for providers and patients. METHODS A training dataset of 41,222 patients was created from the National Inpatient Sample (NIS). 26 M

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Omar, Shakir Hasan, and Ahmed Saleh Ibrahim. "Development of heart attack prediction model based on ensemble learning." Eastern-European Journal of Enterprise Technologies 4, no. 2 (112) (2021): 26–34. https://doi.org/10.15587/1729-4061.2021.238528.

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With the advent of the data age, the continuous improvement and widespread application of medical information systems have led to an exponential growth of biomedical data, such as medical imaging, electronic medical records, biometric tags, and clinical records that have potential and essential research value. However, medical research based on statistical methods is limited by the class and size of the research community, so it cannot effectively perform data mining for large-scale medical information. At the same time, supervised machine learning techniques can effectively solve this problem

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Matushkin, Dmytro. "PHOTOVOLTAIC GENERATION FORECASTING MODELS: CONCEPTUAL ENSEMBLE ARCHITECTURES." System Research in Energy 2024, no. 4 (2024): 56–64. https://doi.org/10.15407/srenergy2024.04.056.

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The decisions regarding power regulation, energy resource planning, and integrating “green” energy into the electrical grid hinge on precise probabilistic forecasts. One of the potential strategies to enhance forecast accuracy is the utilization of ensemble forecasting methods. They represent an approach where multiple models collaborate to achieve superior results compared to what a single model could produce independently. These methods can be categorized into two main categories: competitive and collaborative ensembles. Competitive ensembles harness the diversity of parameters and data to c

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Edmund, De Leon Evangelista, and Descargar Sy Benedict. "An approach for improved students' performance prediction using homogeneous and heterogeneous ensemble methods." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 5226–35. https://doi.org/10.11591/ijece.v12i5.pp5226-5235.

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Web-based learning technologies of educational institutions store a massive amount of interaction data which can be helpful to predict students’ performance through the aid of machine learning algorithms. With this, various researchers focused on studying ensemble learning methods as it is known to improve the predictive accuracy of traditional classification algorithms. This study proposed an approach for enhancing the performance prediction of different single classification algorithms by using them as base classifiers of homogeneous ensembles (bagging and boosting) and heterogeneous e

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Krasnopolsky, Vladimir M., and Ying Lin. "A Neural Network Nonlinear Multimodel Ensemble to Improve Precipitation Forecasts over Continental US." Advances in Meteorology 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/649450.

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A novel multimodel ensemble approach based on learning from data using the neural network (NN) technique is formulated and applied for improving 24-hour precipitation forecasts over the continental US. The developed nonlinear approach allowed us to account for nonlinear correlation between ensemble members and to produce “optimal” forecast represented by a nonlinear NN ensemble mean. The NN approach is compared with the conservative multi-model ensemble, with multiple linear regression ensemble approaches, and with results obtained by human forecasters. The NN multi-model ensemble improves upo

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Eldardiry, Hoda, and Jennifer Neville. "Across-Model Collective Ensemble Classification." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 343–49. http://dx.doi.org/10.1609/aaai.v25i1.7934.

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Ensemble classification methods that independently construct component models (e.g., bagging) improve accuracy over single models by reducing the error due to variance. Some work has been done to extend ensemble techniques for classification in relational domains by taking relational data characteristics or multiple link types into account during model construction. However, since these approaches follow the conventional approach to ensemble learning, they improve performance by reducing the error due to variance in learning. We note however, that variance in inference can be an additional sou

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Emima, A., and D. I. George Amalarethinam. "Integrative Ensemble Learning Algorithm for Predicting Students’ Performance." Indian Journal Of Science And Technology 18, no. 1 (2025): 72–84. https://doi.org/10.17485/ijst/v18i1.3718.

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Objectives: To create a stable student performance prediction model utilizing ensemble learning methods. Methods: The study uses boosting techniques such as CatBoost, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) as simple classifiers, which are then combined into a composite classifier to improve predictive accuracy. During the training phase, a 5-level hyperparameter optimization for the basic classifiers is performed using ETLBO Optimization IELA's distinguishing feature is its Stacking ensemble method, which functions as an ensemble technique, combinin

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Warner, Brandon, Edward Ratner, Kallin Carlous-Khan, Christopher Douglas, and Amaury Lendasse. "Ensemble Learning with Highly Variable Class-Based Performance." Machine Learning and Knowledge Extraction 6, no. 4 (2024): 2149–60. http://dx.doi.org/10.3390/make6040106.

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This paper proposes a novel model-agnostic method for weighting the outputs of base classifiers in machine learning (ML) ensembles. Our approach uses class-based weight coefficients assigned to every output class in each learner in the ensemble. This is particularly useful when the base classifiers have highly variable performance across classes. Our method generates a dense set of coefficients for the models in our ensemble by considering the model performance on each class. We compare our novel method to the commonly used ensemble approaches like voting and weighted averages. In addition, we

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Breskvar, Martin, Dragi Kocev, and Sašo Džeroski. "Ensembles for multi-target regression with random output selections." Machine Learning 107 (July 11, 2018): 1673–709. https://doi.org/10.1007/s10994-018-5744-y.

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We address the task of multi-target regression, where we generate global models that simultaneously predict multiple continuous variables. We use ensembles of generalized decision trees, called predictive clustering trees (PCTs), in particular bagging and random forests (RF) of PCTs and extremely randomized PCTs (extra PCTs). We add another dimension of randomization to these ensemble methods by learning individual base models that consider random subsets of target variables, while leaving the input space randomizations (in RF PCTs and extra PCTs) intact. Moreover, we propose a new ensemble pr

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AlGhamdi, Ahmed Saeed. "Novel Ensemble Model Recommendation Approach for the Detection of Dyslexia." Children 9, no. 9 (2022): 1337. http://dx.doi.org/10.3390/children9091337.

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There are a large number of neurological disorders being explored regarding possible management and treatment, with dyslexia being one of the disorders that affect children at the onset of their learning process. Dyslexia is a developmental neurological disorder that prevents children from learning. The disorder has a prevalence of around 10% across the globe, as reported by most of the literature on dyslexia. The early detection and management of dyslexia is one of the primary pursuits among different research. One such domain that leads this pursuit of the early detection and management of d

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Saphal, Rohan, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, and Bharat Kaul. "ERLP: Ensembles of Reinforcement Learning Policies (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13905–6. http://dx.doi.org/10.1609/aaai.v34i10.7225.

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Reinforcement learning algorithms are sensitive to hyper-parameters and require tuning and tweaking for specific environments for improving performance. Ensembles of reinforcement learning models on the other hand are known to be much more robust and stable. However, training multiple models independently on an environment suffers from high sample complexity. We present here a methodology to create multiple models from a single training instance that can be used in an ensemble through directed perturbation of the model parameters at regular intervals. This allows training a single model that c

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Pham, Doan Tinh, and Thi Ngoc Mai Ta. "Ensemble learning model for Wifi indoor positioning systems." International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 200–206. https://doi.org/10.11591/ijai.v10.i1.pp200-206.

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WiFi indoor positioning researches have received much attention from researchers recently. In this research, we focus on studying the performance of indoor positioning systems that utilize our new proposed ensemble machine learning model. Our new ensemble learning model uses several models for normal data training and position prediction, then it uses the verification data together with its' prediction errors from trained models as the input data to train an intermediate classification model to classify which set of Wifi received signal strength indicator (RSSI) is the best match for each

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J, Arumugam, Raja Sekar S, and Prasanna Venkatesan V. "ENSEMBLE MODEL - BASED BANKRUPTCY PREDICTION." ICTACT Journal on Soft Computing 14, no. 1 (2023): 3147–53. http://dx.doi.org/10.21917/ijsc.2023.0441.

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Bankruptcy prediction is a crucial task in the determination of an organization’s economic condition, that is, whether it can meet its financial obligations or not. It is extensively researched because it includes a crucial impact on staff, customers, management, stockholders, bank disposition assessments, and profitableness. In recent years, Artificial Intelligence and Machine Learning techniques have been widely studied for bankruptcy prediction and Decision-making problems. When it comes to Machine Learning, Artificial Neural Networks perform really well and are extensively used for bankrup

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Ong Yee Hang, Wiwied Virgiyanti, and Rosly Rosaida. "Diabetes Prediction Using Machine Learning Ensemble Model." Journal of Advanced Research in Applied Sciences and Engineering Technology 37, no. 1 (2024): 82–98. http://dx.doi.org/10.37934/araset.37.1.8298.

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Malaysia National Health and Morbidity Survey revealed that one-fifth of Malaysian adults are diagnosed with Diabetes. It exists in different age groups and is hardly discovered especially among youths as the test could only be performed in certain places which require special equipment. It is essential to develop a tool that is capable to generate high accuracy predictions. This research underwent features selection of a secondary dataset which contains seventeen attributes, with no irrelevant data and missing values, and fed it into an AdaBoost with Decision Tree as Base Algorithm Model, Sup

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Han, Xu, Xiaohui Chen, and Li-Ping Liu. "GAN Ensemble for Anomaly Detection." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4090–97. http://dx.doi.org/10.1609/aaai.v35i5.16530.

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When formulated as an unsupervised learning problem, anomaly detection often requires a model to learn the distribution of normal data. Previous works modify Generative Adversarial Networks (GANs) by using encoder-decoders as generators and apply them to anomaly detection tasks. Previous studies indicate that GAN ensembles are often more stable than single GANs in image generation tasks. In this work, we propose to construct GAN ensembles for anomaly detection. In the proposed method, a group of generators interact with a group of discriminators, so every generator gets feedback from every dis

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Üzen, Hüseyin, and Hüseyin Fırat. "DEEP LEARNING-BASED ADAPTIVE ENSEMBLE LEARNING MODEL FOR CLASSIFICATION OF MONKEYPOX DISEASE." Konya Journal of Engineering Sciences 12, no. 4 (2024): 822–37. https://doi.org/10.36306/konjes.1471289.

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Monkeypox a viral disease resembling smallpox often transmitted via animal contact or human-to-human transmission. Symptoms include fever, rash, and respiratory issues. Healthcare experts initially may confuse it with chickenpox or measles due to its rarity, but swollen lymph nodes typically distinguish it. Diagnosis involves tissue sampling and polymerase chain reaction (PCR) testing, although PCR tests have limitations like time consumption and false negatives. Deep learning-based detection offers advantages over PCR, including reduced risk of exposure, quicker results, and improved accuracy

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Pawar, Arti, K. Manjula Shenoy, Srikanth Prabhu, and D. Guruprasad Rai. "Performance analysis of machine learning algorithms: Single Model VS Ensemble Model." Journal of Physics: Conference Series 2571, no. 1 (2023): 012007. http://dx.doi.org/10.1088/1742-6596/2571/1/012007.

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Abstract Machine Learning is a branch of Artificial Intelligence that predicts several naturally occurring events by training a model with some data and then using unseen data to test it. This paper seeks to analyze the performances of single and ensemble machine learning algorithms on the Cleveland Heart disease data set. Experimental study proves that the accuracy score and area under the ROC curve in the ensemble machine learning model is higher than the single machine learning model in predicting non-CVD and CVD patients.

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Bagawade, Ramdas Pandurang, and Thirupurasundari D.R. "Ensemble Machine Learning Techniques." International Journal of Emerging Technology and Advanced Engineering 15, no. 4 (2025): 15–23. https://doi.org/10.46338/ijetae0425_02.

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Ensemble machine learning techniques have emerged as powerful tools to enhance predictive performance and robustness in various applications. By combining multiple base models, ensemble methods leverage the strengths of individual learners while mitigating their weaknesses. This paper explores three principal ensemble strategies: bagging, boosting, and stacking. Through empirical evaluations, we demonstrate the superior performance of ensemble methods over single-model approaches in diverse datasets. Our findings underscore the potential of ensemble techniques to achieve state-of-the-art resul

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Anand, Vatsala, Sheifali Gupta, Deepali Gupta, et al. "Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images." Diagnostics 13, no. 7 (2023): 1320. http://dx.doi.org/10.3390/diagnostics13071320.

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Brain tumor diagnosis at an early stage can improve the chances of successful treatment and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures, such as magnetic resonance imaging (MRI), can be used to diagnose brain tumors. Deep learning, a type of artificial intelligence, can analyze MRI images in a matter of seconds, reducing the time it takes for diagnosis and potentially improving patient outcomes. Furthermore, an ensemble model can help increase the accuracy of classification by combining the strengths of multiple models and compensating for their indi

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P, Muthulakshmi, Parveen M, and Rajeswari P. "Prediction of Heart Disease using Ensemble Learning." Indian Journal of Science and Technology 16, no. 20 (2023): 1469–76. https://doi.org/10.17485/IJST/v16i20.2279.

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Abstract <strong>Objectives:</strong> To propose a Bagging ensemble method to predict heart disease at early stages. The main focus of this research is to increase the prediction accuracy in a model. <strong>Methods:</strong> The proposed system is experimented with by using the Cleveland datasets collected from the UCI repository. The dataset consists of 14 attributes. In this dataset we applied different machine learning algorithms such as Decision tree, Naïve Bayes, Random Forest and SVM along with the proposed ensemble learning classifier. The entire dataset is trained

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Guo, Yan Feng, Na Sun, and Yuan Yao. "An Ensemble Learning Model Based on SOM-SVM Model for Personal Credit Risk." Advanced Materials Research 271-273 (July 2011): 1286–90. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1286.

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Credit risk problem is an essential problem in financial management area. People usually employ personal credit scoring to avoid financial risk problem. Although many methods have been proposed for evaluating the personal credit scoring and obtained good effects, most of these methods were called single model types, which would be disturbed by model self-parameter, data noise and other external factors. In order to overcome the weakness of single model, we believe one of best ways is to construct an ensemble model. In this paper, we proposed a new style of ensemble model and employed two publi

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Lee, Seokjin, Minhan Kim, Seunghyeon Shin, Seungjae Baek, Sooyoung Park, and Youngho Jeong. "Ensemble-Guided Model for Performance Enhancement in Model-Complexity-Limited Acoustic Scene Classification." Applied Sciences 12, no. 1 (2021): 44. http://dx.doi.org/10.3390/app12010044.

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In recent acoustic scene classification (ASC) models, various auxiliary methods to enhance performance have been applied, e.g., subsystem ensembles and data augmentations. Particularly, the ensembles of several submodels may be effective in the ASC models, but there is a problem with increasing the size of the model because it contains several submodels. Therefore, it is hard to be used in model-complexity-limited ASC tasks. In this paper, we would like to find the performance enhancement method while taking advantage of the model ensemble technique without increasing the model size. Our metho

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Lee, Jiann-Der, and Chih Mao Tsai. "Advancing Barrett’s Esophagus Segmentation: A Deep-Learning Ensemble Approach with Data Augmentation and Model Collaboration." Bioengineering 11, no. 1 (2024): 47. http://dx.doi.org/10.3390/bioengineering11010047.

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This approach provides a thorough investigation of Barrett’s esophagus segmentation using deep-learning methods. This study explores various U-Net model variants with different backbone architectures, focusing on how the choice of backbone influences segmentation accuracy. By employing rigorous data augmentation techniques and ensemble strategies, the goal is to achieve precise and robust segmentation results. Key findings include the superiority of DenseNet backbones, the importance of tailored data augmentation, and the adaptability of training U-Net models from scratch. Ensemble methods are

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Bu, Le, Caiping Hu, and Xiuliang Zhang. "Recognition of food images based on transfer learning and ensemble learning." PLOS ONE 19, no. 1 (2024): e0296789. http://dx.doi.org/10.1371/journal.pone.0296789.

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The recognition of food images is of great significance for nutrition monitoring, food retrieval and food recommendation. However, the accuracy of recognition had not been high enough due to the complex background of food images and the characteristics of small inter-class differences and large intra-class differences. To solve these problems, this paper proposed a food image recognition method based on transfer learning and ensemble learning. Firstly, generic image features were extracted by using the convolutional neural network models (VGG19, ResNet50, MobileNet V2, AlexNet) pre-trained on

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Ruvita, Faurina, Wijanarko Andang, Faza Heryuanti Aknia, Ihsani Ishak Sahrial, and Agustian Indra. "Comparative study of ensemble deep learning models to determine the classification of turtle species." Computer Science and Information Technologies 4, no. 1 (2023): 24–32. https://doi.org/10.11591/csit.v4i1.pp24-32.

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Sea turtles are reptiles listed on the international union for conservation of nature (IUCN) red list of threatened species and the convention on international trade in endangered species of wild fauna and flora (CITES) Appendix I as species threatened with extinction. Sea turtles are nearly extinct due to natural predators and people who are frequently incorrect or even ignorant in determining which turtles should not be caught. The aim of this study was to develop a classification system to help classify sea turtle species. Therefore, the ensemble deep learning of convolutional neural networ

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O'Donncha, Fearghal, Yushan Zhang, Bei Chen, and Scott James. "An integrated framework that combines machine learning and numerical models to improve wave-condition forecasts." Journal of Marine Systems 186 (November 1, 2018): 29–36. https://doi.org/10.1016/j.jmarsys.2018.05.006.

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This study investigates near-shore circulation and wave characteristics applied to a case-study site in Monterey Bay, California. We integrate physics-based models to resolve wave conditions together with a machine-learning algorithm that combines forecasts from multiple, independent models into a single “best-estimate” prediction of the true state. The Simulating WAves Nearshore (SWAN) physics-based model is used to compute wind-augmented waves. Ensembles are developed based on multiple simulations perturbing data input to the model. A learning-aggregation technique uses

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Tinh Pham, Doan, and Ta Thi Ngoc Mai. "Ensemble learning model for Wifi indoor positioning systems." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 200. http://dx.doi.org/10.11591/ijai.v10.i1.pp200-206.

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<p>WiFi indoor positioning researches have received much attention from researchers recently. In this research, we focus on studying the performance of indoor positioning systems that utilize our new proposed ensemble machine learning model. Our new ensemble learning model uses several models for normal data training and position prediction, then it uses the verification data together with its' prediction errors from trained models as the input data to train an intermediate classification model to classify which set of Wifi received signal strength indicator (RSSI) is the best match for

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Huang, Haifeng, Lei Huang, Rongjia Song, Feng Jiao, and Tao Ai. "Bus Single-Trip Time Prediction Based on Ensemble Learning." Computational Intelligence and Neuroscience 2022 (August 11, 2022): 1–24. http://dx.doi.org/10.1155/2022/6831167.

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The prediction of bus single-trip time is essential for passenger travel decision-making and bus scheduling. Since many factors could influence bus operations, the accurate prediction of the bus single-trip time faces a great challenge. Moreover, bus single-trip time has obvious nonlinear and seasonal characteristics. Hence, in order to improve the accuracy of bus single-trip time prediction, five prediction algorithms including LSTM (Long Short-term Memory), LR (Linear Regression), KNN (K-Nearest Neighbor), XGBoost (Extreme Gradient Boosting), and GRU (Gate Recurrent Unit) are used and examin

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