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Artykuły w czasopismach na temat „ENSEMBLE LEARNING MODELS”

Autor: Grafiati

Data publikacji: 11 września 2023

Data aktualizacji: 26 lipca 2025

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Imran, Sheik, and Pradeep N. "A Review on Ensemble Machine and Deep Learning Techniques Used in the Classification of Computed Tomography Medical Images." International Journal of Health Sciences and Research 14, no. 1 (2024): 201–13. http://dx.doi.org/10.52403/ijhsr.20240124.

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Ensemble learning combines multiple base models to enhance predictive performance and generalize better on unseen data. In the context of Computed Tomography (CT) image processing, ensemble techniques often leverage diverse machine learning or deep learning architectures to achieve the best results. Ensemble machine learning and deep learning techniques have revolutionized the field of CT image processing by significantly improving accuracy, robustness, and efficiency in various medical imaging tasks. These methods have been instrumental in tasks such as image reconstruction, segmentation, cla

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GURBYCH, A. "METHOD SUPER LEARNING FOR DETERMINATION OF MOLECULAR RELATIONSHIP." Herald of Khmelnytskyi National University. Technical sciences 307, no. 2 (2022): 14–24. http://dx.doi.org/10.31891/2307-5732-2022-307-2-14-24.

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This paper uses the Super Learning principle to predict the molecular affinity between the receptor (large biomolecule) and ligands (small organic molecules). Meta-models study the optimal combination of individual basic models in two consecutive ensembles – classification and regression. Each costume contains six models of machine learning, which are combined by stacking. Base models include the reference vector method, random forest, gradient boosting, neural graph networks, direct propagation, and transformers. The first ensemble predicts binding probability and classifies all candidate mol

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Saqib, Malik, and Sharma Narendra. "A Vast Review of Recognizing the Presence of Android Malware Based on Ensemble Machine Learning Technique." Indian Journal of Science and Technology 17, no. 2 (2024): 149–65. https://doi.org/10.17485/IJST/v17i2.2406.

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Abstract <strong>Background:</strong> It is evaluated that there is 70% to 80% of smartphone users have an Android mobile. Given its trend, a lot of malware strikes on the Android OS. In 2018, the largest number of malware attacks was identified, when there were 10.5 billion such malicious activity detected worldwide. Machine learning has emerged as a promising approach for detecting Android malware, and Ensemble machine learning has been shown to enhance the accuracy of malware detection in other domains. Objectives: In this paper, the systematic literature review were conducted using na

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ACOSTA-MENDOZA, NIUSVEL, ALICIA MORALES-REYES, HUGO JAIR ESCALANTE, and ANDRÉS GAGO-ALONSO. "LEARNING TO ASSEMBLE CLASSIFIERS VIA GENETIC PROGRAMMING." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 07 (2014): 1460005. http://dx.doi.org/10.1142/s0218001414600052.

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This paper introduces a novel approach for building heterogeneous ensembles based on genetic programming (GP). Ensemble learning is a paradigm that aims at combining individual classifier's outputs to improve their performance. Commonly, classifiers outputs are combined by a weighted sum or a voting strategy. However, linear fusion functions may not effectively exploit individual models' redundancy and diversity. In this research, a GP-based approach to learn fusion functions that combine classifiers outputs is proposed. Heterogeneous ensembles are aimed in this study, these models use individ

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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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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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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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Zhang, Yonglin, Lezheng Yu, Li Xue, Fengjuan Liu, Runyu Jing, and Jiesi Luo. "Optimizing lipocalin sequence classification with ensemble deep learning models." PLOS ONE 20, no. 4 (2025): e0319329. https://doi.org/10.1371/journal.pone.0319329.

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Deep learning (DL) has become a powerful tool for the recognition and classification of biological sequences. However, conventional single-architecture models often struggle with suboptimal predictive performance and high computational costs. To address these challenges, we present EnsembleDL-Lipo, an innovative ensemble deep learning framework that combines Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) to enhance the identification of lipocalin sequences. Lipocalins are multifunctional extracellular proteins involved in various diseases and stress responses, and their l

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Alazba, Amal, and Hamoud Aljamaan. "Software Defect Prediction Using Stacking Generalization of Optimized Tree-Based Ensembles." Applied Sciences 12, no. 9 (2022): 4577. http://dx.doi.org/10.3390/app12094577.

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Software defect prediction refers to the automatic identification of defective parts of software through machine learning techniques. Ensemble learning has exhibited excellent prediction outcomes in comparison with individual classifiers. However, most of the previous work utilized ensemble models in the context of software defect prediction with the default hyperparameter values, which are considered suboptimal. In this paper, we investigate the applicability of a stacking ensemble built with fine-tuned tree-based ensembles for defect prediction. We used grid search to optimize the hyperparam

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Ruaud, Albane, Niklas Pfister, Ruth E. Ley, and Nicholas D. Youngblut. "Interpreting tree ensemble machine learning models with endoR." PLOS Computational Biology 18, no. 12 (2022): e1010714. http://dx.doi.org/10.1371/journal.pcbi.1010714.

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Tree ensemble machine learning models are increasingly used in microbiome science as they are compatible with the compositional, high-dimensional, and sparse structure of sequence-based microbiome data. While such models are often good at predicting phenotypes based on microbiome data, they only yield limited insights into how microbial taxa may be associated. We developed endoR, a method to interpret tree ensemble models. First, endoR simplifies the fitted model into a decision ensemble. Then, it extracts information on the importance of individual features and their pairwise interactions, di

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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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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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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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Ramírez-Rivera, Francisco A., and Néstor F. Guerrero-Rodríguez. "Ensemble Learning Algorithms for Solar Radiation Prediction in Santo Domingo: Measurements and Evaluation." Sustainability 16, no. 18 (2024): 8015. http://dx.doi.org/10.3390/su16188015.

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Solar radiation is a fundamental parameter for solar photovoltaic (PV) technology. Reliable solar radiation prediction has become valuable for designing solar PV systems, guaranteeing their performance, operational efficiency, safety in operations, grid dispatchment, and financial planning. However, high quality ground-based solar radiation measurements are scarce, especially for very short-term time horizons. Most existing studies trained machine learning (ML) models using datasets with time horizons of 1 h or 1 day, whereas very few studies reported using a dataset with a 1 min time horizon.

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Li, Ziyue, Kan Ren, Yifan Yang, Xinyang Jiang, Yuqing Yang, and Dongsheng Li. "Towards Inference Efficient Deep Ensemble Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8711–19. http://dx.doi.org/10.1609/aaai.v37i7.26048.

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Ensemble methods can deliver surprising performance gains but also bring significantly higher computational costs, e.g., can be up to 2048X in large-scale ensemble tasks. However, we found that the majority of computations in ensemble methods are redundant. For instance, over 77% of samples in CIFAR-100 dataset can be correctly classified with only a single ResNet-18 model, which indicates that only around 23% of the samples need an ensemble of extra models. To this end, we propose an inference efficient ensemble learning method, to simultaneously optimize for effectiveness and efficiency in e

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Khanna, Samarth, and Kabir Nagpal. "Sign Language Interpretation using Ensembled Deep Learning Models." ITM Web of Conferences 53 (2023): 01003. http://dx.doi.org/10.1051/itmconf/20235301003.

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Communication is an integral part of our day-to-day lives. People experiencing difficulty in speaking or hearing often feel neglected in our society. While Automatic Speech Recognition Systems have now progressed to the purpose of being commercially viable, Signed Language Recognition Systems are still in the early stages. Currently, all such interpretations are administered by humans. Here, we present an approach using ensembled architecture for the classification of Sign Language characters. The novel ensemble of InceptionV3 and ResNet101 achieved an accuracy of 97.24% on the ASL dataset.

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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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Abdillah, Abid Famasya, Cornelius Bagus Purnama Putra, Apriantoni Apriantoni, Safitri Juanita, and Diana Purwitasari. "Ensemble-based Methods for Multi-label Classification on Biomedical Question-Answer Data." Journal of Information Systems Engineering and Business Intelligence 8, no. 1 (2022): 42–50. http://dx.doi.org/10.20473/jisebi.8.1.42-50.

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Background: Question-answer (QA) is a popular method to seek health-related information and biomedical data. Such questions can refer to more than one medical entity (multi-label) so determining the correct tags is not easy. The question classification (QC) mechanism in a QA system can narrow down the answers we are seeking. Objective: This study develops a multi-label classification using the heterogeneous ensembles method to improve accuracy in biomedical data with long text dimensions. Methods: We used the ensemble method with heterogeneous deep learning and machine learning for multi-label

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Padmaja, R., K. V. Satyanarayana, Bonda Dharani, Allu Santhosh Kumar, Kona Dileep Kumar, and Gatta Yamuna. "Pneumonia Detection Using Ensemble Models." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43259.

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Pneumonia remains a leading cause of mortality globally, often challenging to diagnose accurately from chest X-rays due to its similarity to other lung conditions. This study introduces an automated pneumonia detection system leveraging an ensemble of Convolutional Neural Networks (CNNs)—DenseNet121, EfficientNetB0, and ResNet50—combined through a voting classifier. By harnessing the complementary strengths of these models, we achieve enhanced classification accuracy, robustness, and generalization compared to single-model approaches. The system includes a Gradio-based web interface for real-t

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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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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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Sonawane, Deepkanchan Nanasaheb. "Ensemble Learning For Increasing Accuracy Data Models." IOSR Journal of Computer Engineering 9, no. 1 (2013): 35–37. http://dx.doi.org/10.9790/0661-0913537.

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Pasupuleti, Murali Krishna. "AI-Based Intrusion Detection Systems Using Ensemble Deep Learning Models." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 06 (2025): 372–85. https://doi.org/10.62311/nesx/rphcrcscrcp1.

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This study investigates the development and evaluation of AI-based intrusion detection systems (IDS) leveraging ensemble deep learning models. In an era of increasing cyber threats, traditional IDS often struggle with high false positive rates and inadequate adaptability to evolving threats. The proposed research integrates Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Autoencoders within an ensemble framework to enhance anomaly detection in network traffic. Using the NSL-KDD dataset, performance metrics such as accuracy, precision, recall, F1-score, and Area

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Stevens, Christophe AT, Alexander RM Lyons, Kanika I. Dharmayat, et al. "Ensemble machine learning methods in screening electronic health records: A scoping review." DIGITAL HEALTH 9 (January 2023): 205520762311732. http://dx.doi.org/10.1177/20552076231173225.

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Background Electronic health records provide the opportunity to identify undiagnosed individuals likely to have a given disease using machine learning techniques, and who could then benefit from more medical screening and case finding, reducing the number needed to screen with convenience and healthcare cost savings. Ensemble machine learning models combining multiple prediction estimates into one are often said to provide better predictive performances than non-ensemble models. Yet, to our knowledge, no literature review summarises the use and performances of different types of ensemble machi

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Zhang, Lizhou, Siqiao Ye, Deping He, et al. "Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering." Applied Sciences 15, no. 11 (2025): 6192. https://doi.org/10.3390/app15116192.

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Current research lacks an in-depth exploration of ensemble learning and factor engineering applications in regard to landslide susceptibility modeling. In the Three Gorges Reservoir area of China, a region prone to frequent landslides that endanger lives and infrastructure, this study advances landslide susceptibility prediction by integrating ensemble learning with systematic factor engineering. Four homogeneous ensemble models (random forest, XGBoost, LightGBM, and CatBoost) and two heterogeneous ensembles (bagging and stacking) were implemented to evaluate 14 influencing factors. The key re

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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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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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Deore, Bhushan, Aditya Kyatham, and Shubham Narkhede. "A novel approach to ensemble MLP and random forest for network security." ITM Web of Conferences 32 (2020): 03003. http://dx.doi.org/10.1051/itmconf/20203203003.

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The following paper provides a novel approach for Network Intrusion Detection System using Machine Learning and Deep Learning. This approach uses two MLP (Multi-Layer Perceptron) models one having 3 layers and other having 6 layers. Random Forest is also used for classification. These models are ensembled in such a way that the final accuracy is boosted and also the testing time is reduced. Researchers have implemented various ways for the ensemble of multiple models but we are using contradiction management concept to ensemble machine learning models. Contradiction Management concept means if

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Madhavi, Katamaneni, and Laith Abualigah. "Stock market analysis using ensemble learning." Applied and Computational Engineering 17, no. 1 (2023): 226–32. http://dx.doi.org/10.54254/2755-2721/17/20230947.

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A stock market or share market is the combination of shoppers and sellers of shares. Prediction of the stock market is a method for calculating the future value of a company's stock. Stock market can be regarded as a specific records of data mining as well as machine learning problem. The daily changes within the stock depends on the profits and losses and many people think that stock market is irregular and uncertain. Based on daily changes we can predict some movements in the stock. In the previous years, researchers have used many machine learning models to know the development of the stock

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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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Karakaya, İrem. "Evaluation of Machine Learning and Ensemble Learning Models for Classification Using Delivery Data." Verimlilik Dergisi, PRODUCTIVITY FOR LOGISTICS (February 3, 2025): 89–104. https://doi.org/10.51551/verimlilik.1526436.

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Purpose: This study aims to evaluate the performance of various machine learning and ensemble learning models in classifying delivery times using Amazon delivery data. Fast deliveries' role in providing a competitive advantage and boosting customer loyalty highlights the importance of this study. Methodology: The research employs a dataset of 43,739 delivery records with 15 features. Data preprocessing steps include handling missing values, encoding categorical variables, calculating geospatial distances, and normalizing data. Advanced machine learning techniques (e.g., KNN, SVM, Logistic Regr

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Akhi, Sharmin Sultana, Sonjoy Kumar Dey, Mazharul Islam Tusher, et al. "Enhancing Banking Cybersecurity: An Ensemble-Based Predictive Machine Learning Approach." American Journal of Engineering and Technology 07, no. 03 (2025): 88–97. https://doi.org/10.37547/tajet/volume07issue03-07.

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In this study, we propose a predictive cybersecurity framework for the banking sector by integrating ensemble-based machine learning models. Our approach leverages heterogeneous datasets—including internal firewall and intrusion detection system logs, banking transaction records, user behavior data, and external threat intelligence—to capture a comprehensive view of the cyber threat landscape. Following rigorous data preprocessing, feature selection, and feature engineering, we evaluated multiple models, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Deep N

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Campos, David, Miao Zhang, Bin Yang, Tung Kieu, Chenjuan Guo, and Christian S. Jensen. "LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation." Proceedings of the ACM on Management of Data 1, no. 2 (2023): 1–27. http://dx.doi.org/10.1145/3589316.

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Due to the sweeping digitalization of processes, increasingly vast amounts of time series data are being produced. Accurate classification of such time series facilitates decision making in multiple domains. State-of-the-art classification accuracy is often achieved by ensemble learning where results are synthesized from multiple base models. This characteristic implies that ensemble learning needs substantial computing resources, preventing their use in resource-limited environments, such as in edge devices. To extend the applicability of ensemble learning, we propose the LightTS framework th

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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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Praveen, Kumar Rawat. "AI-Powered Drug Discovery Accelerating Pharmaceutical Research and Development." International Journal of Leading Research Publication 6, no. 3 (2025): 1–14. https://doi.org/10.5281/zenodo.15181465.

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AI-driven techniques for drug development have transformed pharmaceutical R&D by significantly shortening the time and minimizing expenditures in evaluating prospective drug candidates. While classical drug discovery is highly experimental-dependent, AI-based procedures use machine learning (ML) and ensemble algorithms to optimize the predicting of molecular properties, drug-target interactions, and virtual screening. This paper discusses the ensemble learning techniques, including Bagging, Boosting (XGBoost, LightGBM), and Stacking, to provide greater accuracy and reliability in drug disc

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Kundan, B., and S. Pushpa. "Comparative Evaluation of Deep Ensemble Models for Multi-Stage Diabetic Retinopathy Severity Assessment." Journal of Neonatal Surgery 14, no. 32S (2025): 698–713. https://doi.org/10.63682/jns.v14i32s.7434.

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Diabetic retinopathy (DR), a progressive retinal vascular disorder associated with diabetes mellitus, is a leading cause of visual impairment and blindness globally. Accurate, early-stage classification and severity grading of DR are critical for timely intervention and treatment planning. Deep learning has shown immense promise in automating DR diagnosis, yet the performance of individual models often varies across datasets and disease stages. This study presents a comparative evaluation of deep ensemble learning strategies to enhance the robustness and accuracy of multi-stage DR severity cla

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Quartulli, Marco, Amaia Gil, Ane Miren Florez-Tapia, Pablo Cereijo, Elixabete Ayerbe, and Igor G. Olaizola. "Ensemble Surrogate Models for Fast LIB Performance Predictions." Energies 14, no. 14 (2021): 4115. http://dx.doi.org/10.3390/en14144115.

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Battery Cell design and control have been widely explored through modeling and simulation. On the one hand, Doyle’s pseudo-two-dimensional (P2D) model and Single Particle Models are among the most popular electrochemical models capable of predicting battery performance and therefore guiding cell characterization. On the other hand, empirical models obtained, for example, by Machine Learning (ML) methods represent a simpler and computationally more efficient complement to electrochemical models and have been widely used for Battery Management System (BMS) control purposes. This article proposes

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Kumar.B, Sathish. "Ensemble Deep Learning Models for Grapevine Leaf Image Classification." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45120.

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The classification of grapevine leaf diseases is crucial for effective vineyard management, as it aids in early detection and treatment of diseases, ultimately improving yield and quality. This paper presents a deep learning-based approach using ensemble models for the classification of grapevine leaf images. By combining multiple deep learning models, the proposed method leverages the strengths of each individual model to improve accuracy, robustness, and generalization. A dataset consisting of images of grapevine leaves is used to evaluate the effectiveness of the ensemble approach. The resu

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Shanmugam, Hemachandiran, and Aghila Gnanasekaran. "Finetuned Deep Learning Models for Fuel Classification: A Transfer Learning-Based Approach." Energies 18, no. 5 (2025): 1176. https://doi.org/10.3390/en18051176.

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Accurately classifying petrol and diesel fuel using an image processing method is crucial for fuel-related industries such as petrol pumps, refineries, and fuel storage facilities. However, distinguishing between these fuels using traditional methods can be challenging due to their similar visual characteristics. This study aims to enhance the accuracy and robustness of existing fuel classification by utilizing the transfer learning-based finetuned pre-trained deep learning models and ensemble approaches. Specifically, we upgrade pre-trained deep models like ResNet152V2, InceptionResNetV2, and

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Kamateri, Eleni, and Michail Salampasis. "An Ensemble Framework for Text Classification." Information 16, no. 2 (2025): 85. https://doi.org/10.3390/info16020085.

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Ensemble learning can improve predictive performance compared to the performance of any of its constituents alone, while keeping computational demands manageable. However, no reference methodology is available for developing ensemble systems. In this paper, we adapt an ensemble framework for patent classification to assist data scientists in creating flexible ensemble architectures for text classification by selecting a finite set of constituent base models from the many available alternatives. We analyze the axes along which someone can select base models of an ensemble system and propose a m

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Qutub, Aseel, Asmaa Al-Mehmadi, Munirah Al-Hssan, Ruyan Aljohani, and Hanan S. Alghamdi. "Prediction of Employee Attrition Using Machine Learning and Ensemble Methods." International Journal of Machine Learning and Computing 11, no. 2 (2021): 110–14. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1022.

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Employees are the most valuable resources for any organization. The cost associated with professional training, the developed loyalty over the years and the sensitivity of some organizational positions, all make it very essential to identify who might leave the organization. Many reasons can lead to employee attrition. In this paper, several machine learning models are developed to automatically and accurately predict employee attrition. IBM attrition dataset is used in this work to train and evaluate machine learning models; namely Decision Tree, Random Forest Regressor, Logistic Regressor, A

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El Bouzekraoui, Meryem, Abdenbi Elaloui, Samira Krimissa, et al. "Performance Assessment of Individual and Ensemble Learning Models for Gully Erosion Susceptibility Mapping in a Mountainous and Semi-Arid Region." Land 13, no. 12 (2024): 2110. https://doi.org/10.3390/land13122110.

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High-accuracy gully erosion susceptibility maps play a crucial role in erosion vulnerability assessment and risk management. The principal purpose of the present research is to evaluate the predictive power of individual machine learning models such as random forest (RF), decision tree (DT), and support vector machine (SVM), and ensemble machine learning approaches such as stacking, voting, bagging, and boosting with k-fold cross validation resampling techniques for modeling gully erosion susceptibility in the Oued El Abid watershed in the Moroccan High Atlas. A dataset comprising 200 gully po

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Roy, Dilip Kumar, Tasnia Hossain Munmun, Chitra Rani Paul, Mohamed Panjarul Haque, Nadhir Al-Ansari, and Mohamed A. Mattar. "Improving Forecasting Accuracy of Multi-Scale Groundwater Level Fluctuations Using a Heterogeneous Ensemble of Machine Learning Algorithms." Water 15, no. 20 (2023): 3624. http://dx.doi.org/10.3390/w15203624.

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Accurate groundwater level (GWL) forecasts are crucial for the efficient utilization, strategic long-term planning, and sustainable management of finite groundwater resources. These resources have a substantial impact on decisions related to irrigation planning, crop selection, and water supply. This study evaluates data-driven models using different machine learning algorithms to forecast GWL fluctuations for one, two, and three weeks ahead in Bangladesh’s Godagari upazila. To address the accuracy limitations inherent in individual forecasting models, a Bayesian model averaging (BMA)-based he

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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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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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Tiago, Pinto, Praça Isabel, Vale Zita, and Silva Jose. "Ensemble learning for electricity consumption forecasting in office buildings." Neurocomputing 423 (May 8, 2020): 747–55. https://doi.org/10.1016/j.neucom.2020.02.124.

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This paper presents three ensemble learning models for short term load forecasting. Machine learning has evolved quickly in recent years, leading to novel and advanced models that are improving the forecasting results in multiple fields. However, in highly dynamic fields such as power and energy systems, dealing with the fast acquisition of large amounts of data from multiple data sources and taking advantage from the correlation between the multiple available variables is a challenging task, for which current models are not prepared. Ensemble learning is bringing promising results in this sen

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Kuo, Ming-Tse, Benny Wei-Yun Hsu, Yi Sheng Lin, et al. "Deep Learning Approach in Image Diagnosis of Pseudomonas Keratitis." Diagnostics 12, no. 12 (2022): 2948. http://dx.doi.org/10.3390/diagnostics12122948.

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This investigation aimed to explore deep learning (DL) models’ potential for diagnosing Pseudomonas keratitis using external eye images. In the retrospective research, the images of bacterial keratitis (BK, n = 929), classified as Pseudomonas (n = 618) and non-Pseudomonas (n = 311) keratitis, were collected. Eight DL algorithms, including ResNet50, DenseNet121, ResNeXt50, SE-ResNet50, and EfficientNets B0 to B3, were adopted as backbone models to train and obtain the best ensemble 2-, 3-, 4-, and 5-DL models. Five-fold cross-validation was used to determine the ability of single and ensemble m

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Wu, Li-Ya, and Sung-Shun Weng. "Ensemble Learning Models for Food Safety Risk Prediction." Sustainability 13, no. 21 (2021): 12291. http://dx.doi.org/10.3390/su132112291.

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Ensemble learning was adopted to design risk prediction models with the aim of improving border inspection methods for food imported into Taiwan. Specifically, we constructed a set of prediction models to enhance the hit rate of non-conforming products, thus strengthening the border control of food products to safeguard public health. Using five algorithms, we developed models to provide recommendations for the risk assessment of each imported food batch. The models were evaluated by constructing a confusion matrix to calculate predictive performance indicators, including the positive predicti

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