Relevant bibliographies by topics / Voting ensemble learning

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Voting ensemble learning'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Voting ensemble learning"

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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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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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Louk, Maya Hilda Lestari, and Bayu Adhi Tama. "PSO-Driven Feature Selection and Hybrid Ensemble for Network Anomaly Detection." Big Data and Cognitive Computing 6, no. 4 (2022): 137. http://dx.doi.org/10.3390/bdcc6040137.

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As a system capable of monitoring and evaluating illegitimate network access, an intrusion detection system (IDS) profoundly impacts information security research. Since machine learning techniques constitute the backbone of IDS, it has been challenging to develop an accurate detection mechanism. This study aims to enhance the detection performance of IDS by using a particle swarm optimization (PSO)-driven feature selection approach and hybrid ensemble. Specifically, the final feature subsets derived from different IDS datasets, i.e., NSL-KDD, UNSW-NB15, and CICIDS-2017, are trained using a hy
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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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Nithin, V. Joe, and Prof S. Pallam Setty. "Prediction of Diabetes Using Ensemble Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (2022): 932–35. http://dx.doi.org/10.22214/ijraset.2022.47114.

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Abstract: Diabetes mellitus is a chronic condition that influences everyday life of the individual having this disease. Diabetes can only be treated to maintain controlled blood glucose levels than to achieve a permanent cure to lead a normal life. As the proverb goes, “prevention is better than cure”, this model aims at “predicting the probability”, of getting this condition, which help early prognosis enough to either avoid it or delay it. Ensemble method is used for prediction of probability of getting diabetes. Classification models in machine learning are used for decision making and enli
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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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Grekov, A. N., and A. A. Kabanov. "Ensemble machine learning methods for Euler angles detection in an inertial navigation system." Monitoring systems of environment, no. 1 (March 28, 2022): 112–20. http://dx.doi.org/10.33075/2220-5861-2022-1-112-120.

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The work is focused on increasing the reliability of navigation information of autonomous platforms used in studying oceans and seas, namely: determining the Euler angles using experimental data generated at the output of an inertial navigation system built on the basis of MEMS sensors. Two ensemble methods of machine learning are considered: majority voting (voting by the majority) and weighted majority voting. The ensembles are formed by combining three supervised learning methods: support vector machine (SVM), k-nearest neighbors (KNN), and decision trees. Optimization of hyperparameters of
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Abdullah, Ahmed Najm. "Development of an Intrusion Detection System using an Ensemble Voting Machine Learning Technique." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 23917–22. https://doi.org/10.48084/etasr.10764.

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Intrusion Detection Systems (IDSs) are essential for identifying unauthorized access and malicious activities in network environments. The current study presents the development of an IDS utilizing a voting-based ensemble Machine Learning (ML) approach. Utilizing the advantages of individual ML models, the voting classifier is a well-known ML model that may enhance overall prediction performance. This study provides a unique classification method that combines the benefits of the Naive Bayes (NB), K-Nearest Neighbors (KNN), and Adaptive Boosting (AdaBoost) algorithms into a voting ensemble app
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Assiri, Adel S., Saima Nazir, and Sergio A. Velastin. "Breast Tumor Classification Using an Ensemble Machine Learning Method." Journal of Imaging 6, no. 6 (2020): 39. http://dx.doi.org/10.3390/jimaging6060039.

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Breast cancer is the most common cause of death for women worldwide. Thus, the ability of artificial intelligence systems to detect possible breast cancer is very important. In this paper, an ensemble classification mechanism is proposed based on a majority voting mechanism. First, the performance of different state-of-the-art machine learning classification algorithms were evaluated for the Wisconsin Breast Cancer Dataset (WBCD). The three best classifiers were then selected based on their F3 score. F3 score is used to emphasize the importance of false negatives (recall) in breast cancer clas
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Nakata, Norio, and Tsuyoshi Siina. "Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic Masses." Bioengineering 10, no. 1 (2023): 69. http://dx.doi.org/10.3390/bioengineering10010069.

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Ultrasound (US) is often used to diagnose liver masses. Ensemble learning has recently been commonly used for image classification, but its detailed methods are not fully optimized. The purpose of this study is to investigate the usefulness and comparison of some ensemble learning and ensemble pruning techniques using multiple convolutional neural network (CNN) trained models for image classification of liver masses in US images. Dataset of the US images were classified into four categories: benign liver tumor (BLT) 6320 images, liver cyst (LCY) 2320 images, metastatic liver cancer (MLC) 9720
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Dissertations / Theses on the topic "Voting ensemble learning"

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Shoemaker, Larry. "Ensemble Learning With Imbalanced Data." Scholar Commons, 2010. http://scholarcommons.usf.edu/etd/3589.

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We describe an ensemble approach to learning salient spatial regions from arbitrarily partitioned simulation data. Ensemble approaches for anomaly detection are also explored. The partitioning comes from the distributed processing requirements of large-scale simulations. The volume of the data is such that classifiers can train only on data local to a given partition. Since the data partition reflects the needs of the simulation, the class statistics can vary from partition to partition. Some classes will likely be missing from some or even most partitions. We combine a fast ensemble learning
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Natarajan, Keerthana. "Integrating Machine Learning with Web Application to Predict Diabetes." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627663657558303.

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Karlsson, Erik, and Gilbert Nordhammar. "Naive semi-supervised deep learning med sammansättning av pseudo-klassificerare." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17177.

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Ett vanligt problem inom supervised learning är brist på taggad träningsdata. Naive semi-supervised deep learning är en träningsteknik som ämnar att mildra detta problem genom att generera pseudo-taggad data och därefter låta ett neuralt nätverk träna på denna samt en mindre mängd taggad data. Detta arbete undersöker om denna teknik kan förbättras genom användandet av röstning. Flera neurala nätverk tränas genom den framtagna tekniken, naive semi-supervised deep learning eller supervised learning och deras träffsäkerhet utvärderas därefter. Resultaten visade nästan enbart försämringar då röstn
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Monteith, Kristine Perry. "Heuristic Weighted Voting." BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/1206.

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Selecting an effective method for combining the votes of classifiers in an ensemble can have a significant impact on the overall classification accuracy an ensemble is able to achieve. With some methods, the ensemble cannot even achieve as high a classification accuracy as the most accurate individual classifying component. To address this issue, we present the strategy of Heuristic Weighted Voting, a technique that uses heuristics to determine the confidence that a classifier has in its predictions on an instance by instance basis. Using these heuristics to weight the votes in an ensemble res
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Dolo, Kgaugelo Moses. "Differential evolution technique on weighted voting stacking ensemble method for credit card fraud detection." Diss., 2019. http://hdl.handle.net/10500/26758.

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Differential Evolution is an optimization technique of stochastic search for a population-based vector, which is powerful and efficient over a continuous space for solving differentiable and non-linear optimization problems. Weighted voting stacking ensemble method is an important technique that combines various classifier models. However, selecting the appropriate weights of classifier models for the correct classification of transactions is a problem. This research study is therefore aimed at exploring whether the Differential Evolution optimization method is a good approach for defining t
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Book chapters on the topic "Voting ensemble learning"

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Raharjo, Agus Budi, and Mohamed Quafafou. "Dynamic Reliable Voting in Ensemble Learning." In IFIP Advances in Information and Communication Technology. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19823-7_14.

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Husain, Adil, and Muneeb H. Khan. "Early Diabetes Prediction Using Voting Based Ensemble Learning." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1810-8_10.

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Mosquera, Marcela, and Remigio Hurtado. "Software Defect Prediction: A Machine Learning Approach with Voting Ensemble." In Proceedings of Ninth International Congress on Information and Communication Technology. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3559-4_47.

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Oulhadj, Mohammed, Jamal Riffi, Chaimae Khodriss, et al. "Diabetic Retinopathy Prediction Based on Transfer Learning and Ensemble Voting." In Digital Technologies and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29857-8_92.

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Wang, Xinglong, and Linning Liu. "Predicting Runway Configurations at Airports Through Soft Voting Ensemble Learning." In Engineering Psychology and Cognitive Ergonomics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60728-8_20.

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Barwal, Akshat, Akshat Rohil, Aman Goyal, and Seba Susan. "Diabetic Retinopathy Detection Using Deep Learning Ensemble with Soft Voting." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3372-2_28.

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Jingming, Liu, Qaiser Khan, and Pantea Keikhosrokiani. "Knowledge Workers Mental Workload Classification Using Voting Ensemble Learning Framework." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-91351-8_22.

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Liu, Jianjun, Shengping Xia, Weidong Hu, and Wenxian Yu. "Weights Updated Voting for Ensemble of Neural Networks Based Incremental Learning." In Advances in Neural Networks – ISNN 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01507-6_75.

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Satyanarayana, S. K., and A. Nageswar Rao. "Solar Power Prediction Using Soft Voting Based Ensemble Machine Learning Classifier." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0767-6_44.

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Miriyala, Geetha Pratyusha, and Arun Kumar Sinha. "Voting Ensemble Learning Technique with Improved Accuracy for the CAD Diagnosis." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5936-3_45.

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Conference papers on the topic "Voting ensemble learning"

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Philip, Merin Susan, Theegala Sindhu, Devu Sai Sathwika, and Guduru Dinesh. "Heart Disease Prediction Using Machine Learning-Based Voting Ensemble." In 2025 3rd International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2025. https://doi.org/10.1109/icici65870.2025.11069509.

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Milosevic, Marija, Vladimir Ciric, and Ivan Milentijevic. "Network Intrusion Detection Using Weighted Voting Ensemble Deep Learning Model." In 2024 11th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN). IEEE, 2024. http://dx.doi.org/10.1109/icetran62308.2024.10645137.

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Muduli, Debendra, Santosh Kumar Sharma, Suryakanta Mahapatra, Debasish Pradhan, Nihar Ranjan Panda, and Sourav Parija. "Ensemble Learning for Diabetes Detection: A Voting-Based Classifier Approach." In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2024. https://doi.org/10.1109/scopes64467.2024.10990576.

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Mohapatra, Subasish, Akanksha Keshari, and Subhadarshini Mohanty. "An Ensemble Learning Framework for Prediction of Diabetes Mellitus using Soft Voting and Hard Voting Framework." In 2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP). IEEE, 2024. https://doi.org/10.1109/aisp61711.2024.10870635.

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Tripathi, Deeksha, Saroj k. Biswas, Akhil Kr Das, Arijit Bhattacharya, and Biswajit Purkayastha. "Strength of Ensemble Learning in Voting Classifier for Crop Yield Prediction." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10726172.

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Ali, Mohammed Ansar, Aswin D, Midhunraj S, Rajesh B, and Sukuna D. "Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm." In 2025 8th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2025. https://doi.org/10.1109/icoei65986.2025.11013159.

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Sanyal, Parambrata, Mukund Kuthe, Sudhanshu Maurya, Kashish Mirza, Pradnya Borkar, and Rachit Garg. "A Voting Ensemble Learning Model for Improved Credit Default Risk Prediction." In 2024 Global Conference on Communications and Information Technologies (GCCIT). IEEE, 2024. https://doi.org/10.1109/gccit63234.2024.10862547.

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Tazwar, Asif, Md Mahir Daiyan, Md Jiabul Hoque, Mohammed Saifuddin, and Md Khaliluzzaman. "Enhancing Spam Email Detection with a Soft Voting Ensemble of Optimized Machine Learning." In 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS). IEEE, 2024. https://doi.org/10.1109/compas60761.2024.10796598.

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Revathi, A., and S. Poonguzhali. "Predictive Modelling of Rice Blast Disease Utilizing Ensemble Voting Classifiers in Machine Learning." In 2024 8th International Conference on Inventive Systems and Control (ICISC). IEEE, 2024. http://dx.doi.org/10.1109/icisc62624.2024.00016.

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S, Shunmuga Priya, and Amuthaguka D. "Heart Disease Prediction: Hyper Parameter Tuning Based Ensemble Learning and Voting Classifiers Approach." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763009.

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