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Journal articles on the topic 'Ensemble learning method'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

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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Osareh, Alireza, and Bita Shadgar. "An Efficient Ensemble Learning Method for Gene Microarray Classification." BioMed Research International 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/478410.

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The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable featu

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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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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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Izonin, Ivan, Roman Muzyka, Roman Tkachenko, Michal Gregus, Roman Korzh, and Kyrylo Yemets. "An enhanced cascade ensemble method for big data analysis." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 963. https://doi.org/10.11591/ijai.v14.i2.pp963-974.

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In the digital age, the proliferation of data presents both challenges and opportunities, particularly in the realm of big data, which is characterized by its volume, velocity, and variety. Machine learning is a crucial technology for extracting insights from these vast datasets. Among machine learning methods, ensemble methods, and especially cascading ensembles, are highly effective for big data analysis. While it is true that the training procedures for cascade ensembles can be time-consuming and may have limitations in terms of accuracy, this paper proposes a solution to enhance their perf

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Ivan, Izonin, Muzyka Roman, Tkachenko Roman, Gregus Michal, Korzh Roman, and Yemets Kyrylo. "An enhanced cascade ensemble method for big data analysis." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 963–74. https://doi.org/10.11591/ijai.v14.i2.pp963-974.

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In the digital age, the proliferation of data presents both challenges and opportunities, particularly in the realm of big data, which is characterized by its volume, velocity, and variety. Machine learning is a crucial technology for extracting insights from these vast datasets. Among machine learning methods, ensemble methods, and especially cascading ensembles, are highly effective for big data analysis. While it is true that the training procedures for cascade ensembles can be time-consuming and may have limitations in terms of accuracy, this paper proposes a solution to enhance their perf

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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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Hart, Emma, and Kevin Sim. "On Constructing Ensembles for Combinatorial Optimisation." Evolutionary Computation 26, no. 1 (2018): 67–87. http://dx.doi.org/10.1162/evco_a_00203.

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Although the use of ensemble methods in machine-learning is ubiquitous due to their proven ability to outperform their constituent algorithms, ensembles of optimisation algorithms have received relatively little attention. Existing approaches lag behind machine-learning in both theory and practice, with no principled design guidelines available. In this article, we address fundamental questions regarding ensemble composition in optimisation using the domain of bin-packing as an example. In particular, we investigate the trade-off between accuracy and diversity, and whether diversity metrics ca

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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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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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TURAN, SELIN CEREN, and MEHMET ALI CENGIZ. "ENSEMBLE LEARNING ALGORITHMS." Journal of Science and Arts 22, no. 2 (2022): 459–70. http://dx.doi.org/10.46939/j.sci.arts-22.2-a18.

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Artificial intelligence is a method that is increasingly becoming widespread in all areas of life and enables machines to imitate human behavior. Machine learning is a subset of artificial intelligence techniques that use statistical methods to enable machines to evolve with experience. As a result of the advancement of technology and developments in the world of science, the interest and need for machine learning is increasing day by day. Human beings use machine learning techniques in their daily life without realizing it. In this study, ensemble learning algorithms, one of the machine learn

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Doganer, Adem. "Different Approaches to Reducing Bias in Classification of Medical Data by Ensemble Learning Methods." International Journal of Big Data and Analytics in Healthcare 6, no. 2 (2021): 15–30. http://dx.doi.org/10.4018/ijbdah.20210701.oa2.

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In this study, different models were created to reduce bias by ensemble learning methods. Reducing the bias error will improve the classification performance. In order to increase the classification performance, the most appropriate ensemble learning method and ideal sample size were investigated. Bias values and learning performances of different ensemble learning methods were compared. AdaBoost ensemble learning method provided the lowest bias value with n: 250 sample size while Stacking ensemble learning method provided the lowest bias value with n: 500, n: 750, n: 1000, n: 2000, n: 4000, n

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Klaar, Anne Carolina Rodrigues, Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, and Leandro dos Santos Coelho. "Structure Optimization of Ensemble Learning Methods and Seasonal Decomposition Approaches to Energy Price Forecasting in Latin America: A Case Study about Mexico." Energies 16, no. 7 (2023): 3184. http://dx.doi.org/10.3390/en16073184.

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The energy price influences the interest in investment, which leads to economic development. An estimate of the future energy price can support the planning of industrial expansions and provide information to avoid times of recession. This paper evaluates adaptive boosting (AdaBoost), bootstrap aggregation (bagging), gradient boosting, histogram-based gradient boosting, and random forest ensemble learning models for forecasting energy prices in Latin America, especially in a case study about Mexico. Seasonal decomposition of the time series is used to reduce unrepresentative variations. The Op

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Whitaker, Tim, and Darrell Whitley. "Prune and Tune Ensembles: Low-Cost Ensemble Learning with Sparse Independent Subnetworks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8638–46. http://dx.doi.org/10.1609/aaai.v36i8.20842.

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Ensemble Learning is an effective method for improving generalization in machine learning. However, as state-of-the-art neural networks grow larger, the computational cost associated with training several independent networks becomes expensive. We introduce a fast, low-cost method for creating diverse ensembles of neural networks without needing to train multiple models from scratch. We do this by first training a single parent network. We then create child networks by cloning the parent and dramatically pruning the parameters of each child to create an ensemble of members with unique and dive

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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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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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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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Sulistya, Yudha Islami, Elsi Titasari Br Bangun, and Dyah Aruming Tyas. "CNN Ensemble Learning Method for Transfer learning: A Review." ILKOM Jurnal Ilmiah 15, no. 1 (2023): 45–63. http://dx.doi.org/10.33096/ilkom.v15i1.1541.45-63.

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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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Evangelista, Edmund De Leon, and Benedict Descargar Sy. "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. http://dx.doi.org/10.11591/ijece.v12i5.pp5226-5235.

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<span lang="EN-US">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)

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Kapil, Divya. "Enhancing MNIST Digit Recognition with Ensemble Learning Techniques." Mathematical Statistician and Engineering Applications 70, no. 2 (2021): 1362–71. http://dx.doi.org/10.17762/msea.v70i2.2328.

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    Abstract The classification task known as MNIST digit recognition involves identifying handwritten numbers into their corresponding values. Although there are numerous approaches proposed for this type of task, they typically face issues in achieving high accuracy. One method that can improve single models' performance is through ensemble learning. The goal of this study is to explore the use of various learning techniques, such as boosting and bagging, in combination with random forest models and decision trees, to improve the performance of MNIST digit recog

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Bilotserkovskyy, V. V., S. G. Udovenko, and L. E. Chala. "Method of neural network recognition of falsified images." Bionics of Intelligence 2, no. 95 (2020): 32–42. http://dx.doi.org/10.30837/bi.2020.2(95).05.

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Methods for generating images falsified using Deepfake technologies and methods for detecting them are considered. A method for detecting falsified images is proposed, based on the joint use of an ensemble of convolutional neural models, the Attention mechanism and a Siamese network learning strategy. The ensembles of models were formed in different ways (using two, three or more components). The result was calculated as the average value of the AUC and LogLoss indices from all the models included in the ensemble. This approach improves the accuracy of convolutional neural network classifiers

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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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Xiong, Haitao. "Ensemble Learning Method for Class Overlapping Problem." Journal of Information and Computational Science 10, no. 4 (2013): 1195–202. http://dx.doi.org/10.12733/jics20101554.

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Kobayashi, Taisuke. "Generalized Consensus Method in Ensemble Imitation Learning." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2024 (2024): 2P2—J09. https://doi.org/10.1299/jsmermd.2024.2p2-j09.

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Ye, Shuai, Ruoyan Zhao, and Xinru Fang. "An Ensemble Learning Method for Dialect Classification." IOP Conference Series: Materials Science and Engineering 569 (August 9, 2019): 052064. http://dx.doi.org/10.1088/1757-899x/569/5/052064.

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Lin, Jin-Ling, Kao-Shing Hwang, Haobin Shi, and Wei Pan. "An ensemble method for inverse reinforcement learning." Information Sciences 512 (February 2020): 518–32. http://dx.doi.org/10.1016/j.ins.2019.09.066.

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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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You, Gui-Rong, Yeou-Ren Shiue, Wei-Chang Yeh, Xi-Li Chen, and Chih-Ming Chen. "A Weighted Ensemble Learning Algorithm Based on Diversity Using a Novel Particle Swarm Optimization Approach." Algorithms 13, no. 10 (2020): 255. http://dx.doi.org/10.3390/a13100255.

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In ensemble learning, accuracy and diversity are the main factors affecting its performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this study, a two-stage weighted ensemble learning method using the particle swarm optimization (PSO) algorithm is proposed to balance the diversity and accuracy in ensemble learning. The first stage is to enhance the diversity of the individual learner, which can be achieved by manipulating the datasets and the input featu

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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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Sydoruk, Mykhailo, and Solomiya Liaskovska. "An Ensemble Method for the Fraud Detection in Transactions." Mathematical and computer modelling. Series: Technical sciences 25 (September 30, 2024): 88–96. http://dx.doi.org/10.32626/2308-5916.2024-25.88-96.

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In today's world, bank fraud has become one of the significant threats to the financial stability and security of clients of financial institutions. The development of technologies, in particular in the field of machine learning, opens up wide opportunities for building effective systems for detecting and preventing fraud in the banking sector [1, 2]. Detecting fraudulent transactions is an important task that requires thoughtful and technological solutions. One of these methods is the use of machine learning approaches and methods. This paper proposes the use of an ensemble method that combin

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Zhou, Peng, Liang Du, Yi-Dong Shen, and Xuejun Li. "Tri-level Robust Clustering Ensemble with Multiple Graph Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (2021): 11125–33. http://dx.doi.org/10.1609/aaai.v35i12.17327.

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Clustering ensemble generates a consensus clustering result by integrating multiple weak base clustering results. Although it often provides more robust results compared with single clustering methods, it still suffers from the robustness problem if it does not treat the unreliability of base results carefully. Conventional clustering ensemble methods often use all data for ensemble, while ignoring the noises or outliers on the data. Although some robust clustering ensemble methods are proposed, which extract the noises on the data, they still characterize the robustness in a single level, and

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Lalduhsaka, R., Nilutpol Bora, and Ajoy Kumar Khan. "Anomaly-Based Intrusion Detection Using Machine Learning." International Journal of Information Security and Privacy 16, no. 1 (2022): 1–15. http://dx.doi.org/10.4018/ijisp.311466.

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Intrusion detection systems were developed to detect any suspicious traffic in the network. Conventional intrusion detection comes with its sets of limitations. The authors aimed to improve anomaly-based intrusion detection using an ensemble approach of machine learning. In this article, CICIDS2017 and CICIDS 2018 datasets have been used for implementing the proposed method. Random forest regressor is used for feature selection. Three machine learning algorithms (i.e., naïve bayes, QDA, and ID3) are selected and combined (ensembled) for their low computational cost. The ensemble algorithm resu

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B. Rokaya, Mahmoud, and Kholod D. Alsufiani. "Ensemble learning based on relative accuracy approach and diversity teams." Bulletin of Electrical Engineering and Informatics 13, no. 3 (2024): 1897–912. http://dx.doi.org/10.11591/eei.v13i3.6003.

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Ensemble learning, which involves combining the opinions of multiple experts to arrive at a better result, has been used for centuries. In this work, a review of the major voting methods in ensemble learning is explored. This work will focus on a new method for combining the results of individual learners. The method depends on the relative accuracy and diversity of teams. Instead of trying to assign weight to each different trainer, the concept of diversity teams is presented. Each team will vote as one player; however, the individual accuracies of each learner still be implemented. The conce

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Teoh, Chin-Wei, Sin-Ban Ho, Khairi Shazwan Dollmat, and Chuie-Hong Tan. "Ensemble-Learning Techniques for Predicting Student Performance on Video-Based Learning." International Journal of Information and Education Technology 12, no. 8 (2022): 741–45. http://dx.doi.org/10.18178/ijiet.2022.12.8.1679.

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The transformation of education norms from face-to-face teaching era to the Massive Open Online Courses (MOOCs) era has promoted the rise of the big data era in educational data. This situation has created an opportunity for an educator to utilize the available data from MOOCs to facilitate student learning and performance. Therefore, this research study aims to introduce three types of ensemble learning methods, which are stacking, boosting, and bagging, to predict student performance. These techniques combine the advantage of feature selection method and Synthetic Minority Oversampling Techn

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Uma, K., and M. Hanumanthappa Dr. "Applying and Improving Accuracy of Heart Disease Prediction Model using Meta-classifiers and Ensemble Learning Methods with Feature Selection." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 2 (2022): 172–76. https://doi.org/10.35940/ijrte.B7189.0711222.

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<strong>Abstract:</strong> Healthcare industry is a significant sector for producing an enormous amount of data daily. The lack of helpful information is the primary motive for introducing machine learning or data mining techniques for extracting the required pattern needed to make a decision. Globally, heart disease is the leading cause of death. Prediction of heart disease early may help the survival of the patient life. This paper explores the machine learning technologies, ensemble learning, and meta-classifier to predict heart disease with feature selection methods to improve the accuracy

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Wei, Yan Yan, and Tao Sheng Li. "An Empirical Study on Feature Subsampling-Based Ensembles." Applied Mechanics and Materials 239-240 (December 2012): 848–52. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.848.

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Feature subsampling techniques help to create diverse for classifiers ensemble. In this article we investigate two feature subsampling-base ensemble methods - Random Subspace Method (RSM) and Rotation Forest Method (RFM) to explore their usability with different learning algorithms and the robust on noise data. The experiments show that RSM with IBK work better than RFM and AdaBoost, and RFM with tree classifier and rule classifier achieve prominent improvement than others. We also find that Logistic algorithm is not suitable for any of the three ensembles. When adding classification noise int

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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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Yang, Jiasheng, Guanfang Wang, Xu Xiao, Meihua Bao, and Geng Tian. "Explainable ensemble learning method for OCT detection with transfer learning." PLOS ONE 19, no. 3 (2024): e0296175. http://dx.doi.org/10.1371/journal.pone.0296175.

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The accuracy and interpretability of artificial intelligence (AI) are crucial for the advancement of optical coherence tomography (OCT) image detection, as it can greatly reduce the manual labor required by clinicians. By prioritizing these aspects during development and application, we can make significant progress towards streamlining the clinical workflow. In this paper, we propose an explainable ensemble approach that utilizes transfer learning to detect fundus lesion diseases through OCT imaging. Our study utilized a publicly available OCT dataset consisting of normal subjects, patients w

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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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Tri, Okta Priasni, and Oswari Teddy. "Comparative study of standalone classifier and ensemble classifier." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 5 (2021): 1747–54. https://doi.org/10.12928/telkomnika.v19i5.19508.

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Ensemble learning is one of machine learning method that can solve performance measurement problem. Standalone classifiers often show a poor performance result, thus why combining them with ensemble methods can improve their performance scores. Ensemble learning has several methods, in this study, three methods of ensemble learning are compared with standalone classifiers of support vector machine, Naïve Bayes, and decision tree. bagging, AdaBoost, and voting are the ensemble methods that are combined then compared to standalone classifiers. From 1670 dataset of twitter mentions about tou

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Reza Ghaemi. "Pattern Ensemble Learning Method for Clustering Ensemble using Incremental Genetic-Based Algorithm." Power System Technology 49, no. 1 (2025): 24–52. https://doi.org/10.52783/pst.1389.

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The clustering ensemble has emerged as a prominent method for improving clustering accuracy of unsupervised classification. It combines multiple partitions generated by different clustering algorithms into a single clustering solution. Genetic algorithms are known as methods with high ability to solve optimization problems including clustering. To date, significant progress has been contributed to find consensus clustering that will yield better results than existing clustering. This paper has proposed an Incremental Genetic-Based Algorithm for Clustering Ensemble (IGCE) to perform the search

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Ramakrishnan, R., and A. Chirputkar. "Ensemble Learning method for improving the Healthcare IoT System." CARDIOMETRY, no. 25 (February 14, 2023): 171–77. http://dx.doi.org/10.18137/cardiometry.2022.25.171177.

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Wireless Body Area Network (WBAN) is several wearable sensor nodes with unstable sensing, storage, computation, and communication capabilities. Heart infection is an essential origin of death internationally, and early recognition is vital in avoiding the development of the infection. This article presents an Ensemble Learning (EL) method for improving the Healthcare Internet of Things (IoT) System. This approach aims to forecast the risk of heart infection. This method involves dividing the dataset into subgroups at random using a mean-based split. The data is then divided into multiple group

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P. J., Anu, and K. Ranjith Singh. "Ensemble and deep learning via median method for learning disability classification." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2031–41. https://doi.org/10.11591/eei.v14i3.8639.

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The study explores the classification of students with and without learning disabilities (LD) through machine learning techniques, utilizing a real dataset and implementing bootstrapping for data augmentation. Noteworthy findings reveal the Adam optimizer's superior performance among various optimizers, achieving a true positive rate (TPR) of 0.97 and a false positive rate (FPR) of 0.02, with high precision, recall, and f1-score values. Additionally, ensemble learning, employing the median method, combines models like Random-ForestClassifier and KerasClassifier, and BaggingClassifier with Kera

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Hornostal, Oleksii, and Svitlana Gavrylenko. "APPLICATION OF HETEROGENEOUS ENSEMBLES IN PROBLEMS OF COMPUTER SYSTEM STATE IDENTIFICATION." Advanced Information Systems 7, no. 4 (2023): 5–12. http://dx.doi.org/10.20998/2522-9052.2023.4.01.

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The object of the study is the process of identifying anomalies in the operation of a computer system (CS). The subject of the study is ensemble methods for identifying the state of the CS. The goal of the study is to improve the performance of ensemble classifiers based on heterogeneous models. Methods used: machine learning methods, homogeneous and heterogeneous ensemble classifiers, Pasting and Bootstrapping technologies. Results obtained: a comparative analysis of the use of homogeneous and heterogeneous bagging ensembles in data classification problems was carried out. The effectiveness o

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Piyush, Piyush, Nasib Singh .., Priti Maheshwary, Shraddha V. .., Preeti .., and Piyush Kumar Pareek. "An Ensemble Machine Learning Method for Analyzing Various Medical Datasets." Journal of Intelligent Systems and Internet of Things 13, no. 1 (2024): 177–95. http://dx.doi.org/10.54216/jisiot.130114.

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In recent years, machine learning (ML) has shown a significant impact in tackling various complicated problems in different application domains, including healthcare, economics, ecological, stock market, surveillance, and commercial applications. Machine Learning techniques are good enough to deal with a wide range of data, uncover fascinating links, offer insights, and spot trends. ML can improve disease diagnosis accuracy, predictability, performance, and reliability. This paper reviews various machine learning techniques applied to different medical datasets and proposes an ensemble method

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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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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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NAKAI, Gaku, Tomoharu NAKASHIMA, and Hisao ISHIBUCHI. "A Fuzzy Ensemble Learning Method for Pattern Classification." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 15, no. 6 (2003): 671–81. http://dx.doi.org/10.3156/jsoft.15.671.

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Choundaly, Souksavanh, and Dr Wanida Kanarkard. "An Ensemble Method of Multiple Machines Learning System." Khon Kaen University Journal (Graduate Studies) 13, no. 3 (2013): 1–13. http://dx.doi.org/10.5481/kkujgs.2013.13.3.1.

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