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Journal articles on the topic 'Voting ensemble learning'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

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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Sourabh, Mansotra Vibhakar, Kour Paramjit, and Kumar Sachin. "Voting-Boosting: A novel machine learning ensemble for the prediction of Infants' Data." Indian Journal of Science and Technology 13, no. 22 (2020): 2189–202. https://doi.org/10.17485/IJST/v13i22.468.

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Abstract <strong>Background/Objectives:</strong> Owing to the continuous increase of electronic records and recent advances in machine learning, various automated disease diagnosis tools have been developed and proposed in healthcare sector. In the present study, an ensemble methodology using voting and boosting techniques has been proposed for optimal selection of features and prediction of infants' data of India. <strong>Methods/Analysis:</strong> For feature selection, the best-first search algorithm of wrapper technique has been used in addition to votingboosting. The propos

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Jiang, Zhen, and Yong-Zhao Zhan. "A Novel Diversity-Based Semi-Supervised Learning Framework with Related Theoretical Analysis." International Journal on Artificial Intelligence Tools 24, no. 03 (2015): 1550011. http://dx.doi.org/10.1142/s0218213015500116.

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We present a new co-training style framework and combine it with ensemble learning to further improve the generalization ability. By employing different strategies to combine co-training with ensemble learning, two learning algorithms, Sequential Ensemble Co-Learning (SECL) and Parallel Ensemble Co-Learning (PECL) are developed. Furthermore, we propose a weighted bagging method in PECL to generate an ensemble of diverse classifiers at the end of co-training. Finally, based on the voting margin, an upper bound on the generalization error of multi-classifier voting systems is given in the presen

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Airlangga, Gregorius. "Comparative Analysis of Voting and Stacking Ensemble Learning for Heart Disease Prediction: A Machine Learning Approach." Jurnal Teknologi Informatika dan Komputer 11, no. 1 (2025): 362–77. https://doi.org/10.37012/jtik.v11i1.2584.

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Heart disease remains a leading cause of mortality worldwide, necessitating the development of accurate predictive models for early diagnosis and intervention. This study investigates the effectiveness of ensemble learning approaches, particularly Voting and Stacking classifiers, in comparison to traditional machine learning models and deep learning architectures. Using a dataset containing clinical and diagnostic attributes, preprocessing steps such as label encoding and standardization were applied to ensure compatibility with machine learning models. The ensemble classifiers were constructe

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Kurniati, Florentina Tatrin, Hindriyanto Dwi Purnomo, Irwan Sembiring, and Ade Iriani. "Digital Image Object Detection with GLCM Multi-Degrees and Ensemble Learning." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 8, no. 2 (2024): 321–27. http://dx.doi.org/10.29207/resti.v8i2.5597.

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Object detection in digital images has been implemented in various fields. Object detection faces challenges, one of which is rotation problems, causing objects to become unknown. We need a method that can extract features that do not affect rotation and reliable ensemble-based classification. The proposal uses the GLCM-MD (Gray-Level Co-occurrence Matrix Multi-Degrees) extraction method with classification using K-Nearest Neighbours (K-NN) and Random Forest (RF) learning as well as Voting Ensemble (VE) from two single classifications. The main goal is to overcome the difficulty of detecting o

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

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

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Khairy, Rihab, Ameer Hussein, and Haider ALRikabi. "The Detection of Counterfeit Banknotes Using Ensemble Learning Techniques of AdaBoost and Voting." International Journal of Intelligent Engineering and Systems 14, no. 1 (2021): 326–39. http://dx.doi.org/10.22266/ijies2021.0228.31.

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The movement of cash flow transactions by either electronic channels or physically created openings for the influx of counterfeit banknotes in financial markets. Aided by global economic integration and expanding international trade, attention must be geared at robust techniques for the recognition and detection of counterfeit banknotes. This paper presents ensemble learning algorithms for banknotes detection. The AdaBoost and voting ensemble are deployed in combination with machine learning algorithms. Improved detection accuracies are produced by the ensemble methods. Simulation results cert

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Chen, Shikun, and Wenlong Zheng. "RRMSE-enhanced weighted voting regressor for improved ensemble regression." PLOS ONE 20, no. 3 (2025): e0319515. https://doi.org/10.1371/journal.pone.0319515.

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Ensemble regression methods are widely used to improve prediction accuracy by combining multiple regression models, especially when dealing with continuous numerical targets. However, most ensemble voting regressors use equal weights for each base model’s predictions, which can limit their effectiveness, particularly when there is no specific domain knowledge to guide the weighting. This uniform weighting approach doesn’t consider that some models may perform better than others on different datasets, leaving room for improvement in optimizing ensemble performance. To overcome this limitation,

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Butploy, Narut, Wanida Kanarkard, Pewpan M. Intapan, and Oranuch Sanpool. "An Approach for Egg Parasite Classification Based on Ensemble Deep Learning." Journal of Advanced Computational Intelligence and Intelligent Informatics 27, no. 6 (2023): 1113–21. http://dx.doi.org/10.20965/jaciii.2023.p1113.

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Opisthorchis viverrini and minute intestinal fluke (MIF) infections are heavily epidemic in northeastern Thailand. Their primary cause is eating raw or undercooked cyprinid fishes, and they cause health problems in the human digestive system. In cases of liver fluke, these parasites can go through the bile duct system, which may cause cholangiocarcinoma (bile duct cancer). When a medical doctor suspects that a patient is infected with parasites, they typically request a stool analysis to determine the type of egg parasites using microscopy. Both parasites have similar characteristics, thus, it

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GR, Ashisha, Anitha Mary X, and Mahimai Raja J. "Classification of Diabetes Using Ensemble Machine Learning Techniques." Scalable Computing: Practice and Experience 25, no. 4 (2024): 3172–80. http://dx.doi.org/10.12694/scpe.v25i4.2873.

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Diabetes is a widespread chronic condition that impacts people all over the globe and requires a clear and timely diagnosis. Untreated diabetes leads to retinopathy, nephropathy, and damage to the nervous system. In this context, Machine Learning (ML) might be used to detect health problems early, diagnose them, and track their progress. Ensemble techniques are a promising approach that combines many classifiers to improve forecast accuracy and resilience. This study investigates the categorization of diabetes using an ensemble machine learning technique known as a voting classifier. Using a v

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Wu, Xiaofang, Cunhan Guo, Junwu Lin, Zhenheng Lin, and Qun Chen. "Mixed attention ensemble for esophageal motility disorders classification." PLOS ONE 20, no. 2 (2025): e0317912. https://doi.org/10.1371/journal.pone.0317912.

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Esophageal motility disorders result from dysfunction of the lower esophageal sphincter and abnormalities in esophageal peristalsis, often presenting symptoms such as dysphagia, chest pain, or heartburn. High-resolution esophageal manometry currently serves as the primary diagnostic method for these disorders, but it has some shortcomings including technical complexity, high demands on diagnosticians, and time-consuming diagnostic process. Therefore, based on ensemble learning with a mixed voting mechanism and multi-dimensional attention enhancement mechanism, a classification model for esopha

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Fieri, Brillian, and Derwin Suhartono. "Offensive Language Detection Using Soft Voting Ensemble Model." MENDEL 29, no. 1 (2023): 1–6. http://dx.doi.org/10.13164/mendel.2023.1.001.

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Offensive language is one of the problems that have become increasingly severe along with the rise of the internet and social media usage. This language can be used to attack a person or specific groups. Automatic moderation, such as the usage of machine learning, can help detect and filter this particular language for someone who needs it. This study focuses on improving the performance of the soft voting classifier to detect offensive language by experimenting with the combinations of the soft voting estimators. The model was applied to a Twitter dataset that was augmented using several augm

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Hoanh-Su, Le, Le Quang Chan Phong, Truong Cong Vinh, Ho Mai Minh Nhat, and Jong-Hwa Lee. "Default Prediction in the Finance Industry Based on Ensemble Learning: Combining Machine Learning and Deep Learning." Business Systems Research Journal 16, no. 1 (2025): 198–218. https://doi.org/10.2478/bsrj-2025-0010.

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Abstract Background Financial institutions face significant challenges in predicting loan defaults, which directly impact the non-performing loan (NPL) rate. Incorrect predictions can lead to misinformed decisions and substantial financial losses. Objectives This study aims to enhance default prediction by employing advanced ensemble learning techniques in machine learning and deep learning. Methods/Approach Instead of relying on transformation, fine-tuning, or single algorithm models, this research focuses on combining multiple models using voting and stacking techniques, particularly highlig

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Rojarath, Artitayapron, and Wararat Songpan. "Probability-Weighted Voting Ensemble Learning for Classification Model." Journal of Advances in Information Technology 11, no. 4 (2020): 217–27. http://dx.doi.org/10.12720/jait.11.4.217-227.

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Arrohman, Ramadhan Ridho, and Riza Arifudin. "Stock Return Prediction Using Voting Regressor Ensemble Learning." Recursive Journal of Informatics 1, no. 2 (2023): 55–63. http://dx.doi.org/10.15294/rji.v1i2.68048.

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Abstract. The value of return on stock prices is often used in predicting profits in the process of buying and selling shares based on the calculation of the return on investment. The calculation of the value of return on stock prices can be predicted automatically at certain periods, both weekly and daily Purpose: The problem faced is determining a good algorithm for making predictions due to fluctuating data on stock prices making it difficult to predict. Methods: The stages carried out by the researcher include the data preprocessing stage and then proceed to the Exploratory Dat

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Sun, Xiao Wei, and Hong Bo Zhou. "Research on Applied Technology in Experiments with Three Boosting Algorithms." Advanced Materials Research 908 (March 2014): 513–16. http://dx.doi.org/10.4028/www.scientific.net/amr.908.513.

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Boosting algorithms are a means of building a strong ensemble classifier by aggregating a sequence of weak hypotheses. An ensemble consists of a set of independently trained classifiers whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classifiers in the ensemble. In this paper we use applied technology to built an ensemble using a voting methodology of Boosting-BAN and Boosting-MultiTAN ensembles with 10 sub-classifiers in each one. We performed a comparison with Boosting-BAN a

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Kurilová, Veronika, Szabolcs Rajcsányi, Zuzana Rábeková, Jarmila Pavlovičová, Miloš Oravec, and Nora Majtánová. "Detecting glaucoma from fundus images using ensemble learning." Journal of Electrical Engineering 74, no. 4 (2023): 328–35. http://dx.doi.org/10.2478/jee-2023-0040.

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Abstract Glaucomatous changes of the optic nerve head could be detected from fundus images. Focusing on optic nerve head appearance, and its difference from healthy images, altogether with the availability of plenty of such images in public fundus image databases, these images are ideal sources for artificial intelligence methods applications. In this work, we used ensemble learning methods and compared them with various single CNN models (VGG-16, ResNet-50, and MobileNet). The models were trained on images from REFUGE public dataset. The average voting ensemble method outperformed all mention

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Kurniati, Florentina Tatrin, Daniel HF Manongga, Eko Sediyono, Sri Yulianto Joko Prasetyo, and Roy Rudolf Huizen. "Object Classification Model Using Ensemble Learning with Gray-Level Co-Occurrence Matrix and Histogram Extraction." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 9, no. 3 (2023): 793–801. https://doi.org/10.26555/jiteki.v9i3.26683.

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In the field of object classification, identification based on object variations is a challenge in itself. Variations include shape, size, color, and texture, these can cause problems in recognizing and distinguishing objects accurately. The purpose of this research is to develop a classification method so that objects can be accurately identified. The proposed classification model uses Voting and Combined Classifier, with Random Forest, K-NN, Decision Tree, SVM, and Naive Bayes classification methods. The test results show that the voting method and Combined Classifier obtain quite good resul

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Paul, M. Robin Raj, and Dr K. Santhi Sree. "Ensemble Based Detection of Phishing URLs Using Hybrid, Deep Learning and Machine Learning Models." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 6402–15. https://doi.org/10.22214/ijraset.2025.71708.

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Abstract: Phishing attacks pose a serious cybersecurity threat, requiring advanced detection mechanisms. This study proposes an ensemble-based phishing Uniform Resource Locator(URL) detection framework integrating both machine learning and deep learning models. The first phase employs Adaboost, Naïve Bayes(NB), Random Forest(RF), Logistic Regression(LR), Support Vector Machine(SVM), Artificial Neural Network(ANN), Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Long Short TermMemory(LSTM) and Stacked Gated Recurrent Unit(Stacked GRU), combined using voting ensemble. The secon

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Vermani, Kunal, Amandeep Noliya, Sunil Kumar, and Kamlesh Dutta. "Ensemble Learning Based Malicious Node Detection in SDN-Based VANETs." Journal of Information Systems Engineering and Business Intelligence 9, no. 2 (2023): 136–46. http://dx.doi.org/10.20473/jisebi.9.2.136-146.

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Background: The architecture of Software Defined Networking (SDN) integrated with Vehicular Ad-hoc Networks (VANETs) is considered a practical method for handling large-scale, dynamic, heterogeneous vehicular networks, since it offers flexibility, programmability, scalability, and a global understanding. However, the integration with VANETs introduces additional security vulnerabilities due to the deployment of a logically centralized control mechanism. These security attacks are classified as internal and external based on the nature of the attacker. The method adopted in this work facilitate

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Krishnamoorthy, Latha, and Ammasandra Sadashivaiah Raju. "An ensemble approach for electrocardiogram and lip features based biometric authentication by using grey wolf optimization." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1524. http://dx.doi.org/10.11591/ijeecs.v33.i3.pp1524-1535.

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In the pursuit of fortified security measures, the convergence of multimodal biometric authentication and ensemble learning techniques have emerged as a pivotal domain of research. This study explores the integration of multimodal biometric authentication and ensemble learning techniques to enhance security. Focusing on lip movement and electrocardiogram (ECG) data, the research combines their distinct characteristics for advanced authentication. Ensemble learning merges diverse models, achieving increased accuracy and resilience in multimodal fusion. Harmonizing lip and ECG modalities establi

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Krishnamoorthy, Latha, and Ammasandra Sadashivaiah Raju. "An ensemble approach for electrocardiogram and lip features based biometric authentication by using grey wolf optimization." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1524–35. https://doi.org/10.11591/ijeecs.v33.i3.pp1524-1535.

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In the pursuit of fortified security measures, the convergence of multimodal biometric authentication and ensemble learning techniques have emerged as a pivotal domain of research. This study explores the integration of multimodal biometric authentication and ensemble learning techniques to enhance security. Focusing on lip movement and electrocardiogram (ECG) data, the research combines their distinct characteristics for advanced authentication. Ensemble learning merges diverse models, achieving increased accuracy and resilience in multimodal fusion. Harmonizing lip and ECG modalities establi

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Landage, Jyoti H., and Mahesh P. Wankhade. "Ensemble-based Malware Detection with Different Voting Schemes." COMPUSOFT: An International Journal of Advanced Computer Technology 03, no. 10 (2014): 1116–23. https://doi.org/10.5281/zenodo.14759319.

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Now a day’s computer security is the field which attempts to keep information on the computer safe and secure. Security means permitting things you do want, while preventing things you don't want from happening. Malware represents a serious threat to security of computer system. Traditional anti-malware products use the signature-based, heuristic-based detection techniques to detect the malware. These techniques detect the known malware accurately but can't detect the new, unknown malware. This paper presents a malware detection system based on the data mining and machine learning techni

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Umer, Muhammad, Mahum Naveed, Fadwa Alrowais, et al. "Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm." Cancers 14, no. 23 (2022): 6015. http://dx.doi.org/10.3390/cancers14236015.

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Breast cancer is a common cause of female mortality in developing countries. Screening and early diagnosis can play an important role in the prevention and treatment of these cancers. This study proposes an ensemble learning-based voting classifier that combines the logistic regression and stochastic gradient descent classifier with deep convoluted features for the accurate detection of cancerous patients. Deep convoluted features are extracted from the microscopic features and fed to the ensemble voting classifier. This idea provides an optimized framework that accurately classifies malignant

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Andreyestha, Andreyestha, and Agus Subekti. "ANALISA SENTIMENT PADA ULASAN FILM DENGAN OPTIMASI ENSEMBLE LEARNING." Jurnal Informatika 7, no. 1 (2020): 15–23. http://dx.doi.org/10.31311/ji.v7i1.6171.

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Dalam dunia hiburan khususnya film, kini situs web ulasan film menjadi media bagi orang-orang untuk memberikan penilaian mengenai seberapa bagus film tersebut. Mereka tidak harus menjadi pakar dalam dunia perfilman untuk menilai kualitas dari film yang mereka saksikan, semua orang dapat memberikan penilaian. Sentimen yang ditemukan dalam komentar, umpan balik atau kritik memberikan indikator yang berguna untuk berbagai tujuan dan dapat dikategorikan berdasarkan polaritas, polaritas tersebut cenderung akan dicari tahu apakah secara keseluruhan positif atau negatif. Algoritma Naïve Bayes dan Ran

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Walambe, Rahee, Aboli Marathe, and Ketan Kotecha. "Multiscale Object Detection from Drone Imagery Using Ensemble Transfer Learning." Drones 5, no. 3 (2021): 66. http://dx.doi.org/10.3390/drones5030066.

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Object detection in uncrewed aerial vehicle (UAV) images has been a longstanding challenge in the field of computer vision. Specifically, object detection in drone images is a complex task due to objects of various scales such as humans, buildings, water bodies, and hills. In this paper, we present an implementation of ensemble transfer learning to enhance the performance of the base models for multiscale object detection in drone imagery. Combined with a test-time augmentation pipeline, the algorithm combines different models and applies voting strategies to detect objects of various scales i

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Rudini, Edwin, and Ferda Ernawan. "Prediction of Alzheimer's Dementia Using Soft Voting Ensemble Learning with Machine Learning." IJACI : International Journal of Advanced Computing and Informatics 1, no. 1 (2025): 48–55. https://doi.org/10.71129/ijaci.v1.i1.pp48-55.

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Alzheimer's dementia (AD) is a degenerative brain disease characterized by a decline in cognitive function and memory. Predicting AD is crucial for preventing the disease from becoming more severe. Machine learning algorithms can aid in the early prediction of AD. The aim of this study is to develop a predictive model with improved accuracy using ensemble learning methods and machine learning algorithms. The experiment used the Oasis Longitudinal dataset from Oasis Brains, which includes details of patients with and without AD. This study proposed a binary classification using an ensemble lear

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Li, Weiming, Siqi Yu, Runhuang Yang, et al. "Machine Learning Model of ResNet50-Ensemble Voting for Malignant–Benign Small Pulmonary Nodule Classification on Computed Tomography Images." Cancers 15, no. 22 (2023): 5417. http://dx.doi.org/10.3390/cancers15225417.

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Background: The early detection of benign and malignant lung tumors enabled patients to diagnose lesions and implement appropriate health measures earlier, dramatically improving lung cancer patients’ quality of living. Machine learning methods performed admirably when recognizing small benign and malignant lung nodules. However, exploration and investigation are required to fully leverage the potential of machine learning in distinguishing between benign and malignant small lung nodules. Objective: The aim of this study was to develop and evaluate the ResNet50-Ensemble Voting model for detect

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Sahoo, Ghanashyam, Ajit Kumar Nayak, Pradyumna Kumar Tripathy, et al. "Predicting Breast Cancer Relapse from Histopathological Images with Ensemble Machine Learning Models." Current Oncology 31, no. 11 (2024): 6577–97. http://dx.doi.org/10.3390/curroncol31110486.

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Relapse and metastasis occur in 30–40% of breast cancer patients, even after targeted treatments like trastuzumab for HER2-positive breast cancer. Accurate individual prognosis is essential for determining appropriate adjuvant treatment and early intervention. This study aims to enhance relapse and metastasis prediction using an innovative framework with machine learning (ML) and ensemble learning (EL) techniques. The developed framework is analyzed using The Cancer Genome Atlas (TCGA) data, which has 123 HER2-positive breast cancer patients. Our two-stage experimental approach first applied s

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Oner, Mahir, and Alp Ustundag. "Combining predictive base models using deep ensemble learning." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 6657–68. http://dx.doi.org/10.3233/jifs-189126.

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Since information science and communication technologies had improved significantly, data volumes had expanded. As a result of that situation, advanced pre-processing and analysis of collected data became a crucial topic for extracting meaningful patterns hidden in the data. Therefore, traditional machine learning algorithms generally fail to gather satisfactory results when analyzing complex data. The main reason of this situation is the difficulty of capturing multiple characteristics of the high dimensional data. Within this scope, ensemble learning enables the integration of diversified si

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Hamori, Hitoshi, and Shigeyuki Hamori. "Does Ensemble Learning Always Lead to Better Forecasts?" Applied Economics and Finance 7, no. 2 (2020): 51. http://dx.doi.org/10.11114/aef.v7i2.4716.

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Ensemble learning is a common machine learning technique applied to business and economic analysis in which several classifiers are combined using majority voting for better forecasts as compared to those of individual classifier. This study presents a counterexample, which demonstrates that ensemble learning leads to worse classifications than those from individual classifiers, using two events and three classifiers. If there is an outstanding classifier, we should follow its forecast instead of using ensemble learning.

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Salah, Zaher, Hamza Abu Owida, Esraa Abu Elsoud, Esraa Alhenawi, Suhaila Abuowaida, and Nawaf Alshdaifat. "An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form." Future Internet 16, no. 11 (2024): 414. http://dx.doi.org/10.3390/fi16110414.

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Phishing email assaults have been a prevalent cybercriminal tactic for many decades. Various detectors have been suggested over time that rely on textual information. However, to address the growing prevalence of phishing emails, more sophisticated techniques are required to use all aspects of emails to improve the detection capabilities of machine learning classifiers. This paper presents a novel approach to detecting phishing emails. The proposed methodology combines ensemble learning techniques with various variables, such as word frequency, the presence of specific keywords or phrases, and

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Natha, Priya, and Pothuraju RajaRajeswari. "Advancing Skin Cancer Prediction Using Ensemble Models." Computers 13, no. 7 (2024): 157. http://dx.doi.org/10.3390/computers13070157.

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There are many different kinds of skin cancer, and an early and precise diagnosis is crucial because skin cancer is both frequent and deadly. The key to effective treatment is accurately classifying the various skin cancers, which have unique traits. Dermoscopy and other advanced imaging techniques have enhanced early detection by providing detailed images of lesions. However, accurately interpreting these images to distinguish between benign and malignant tumors remains a difficult task. Improved predictive modeling techniques are necessary due to the frequent occurrence of erroneous and inco

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Gupta, Priyanka, and Seth D.D. "Improving the Prediction of Heart Disease Using Ensemble Learning and Feature Selection." International Journal of Advances in Soft Computing and its Applications 14, no. 2 (2022): 37–40. http://dx.doi.org/10.15849/ijasca.220720.03.

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Heart or cardiovascular disease is main cause of mortality. The main objective of developing the proposed model is to increase the accuracy and reliability of predicting the coronary heart disease. This paper attempts in predicting the risk of heart disease more accurately using the techniques of ensemble learning. Moreover, the techniques of feature selection and hyper parameter tuning has been implemented in this work leading to further increase in accuracy. Among the three ensemble techniques, stacking, majority voting and bagging used in this work, the improvement achieved in prediction ac

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Kurniawan, Sandy, Adhe Setya Pramayoga, and Yeva Fadhilah Ashari. "An Ensemble-Based Approach for Detecting Clickbait in Indonesian Online Media." Jurnal Masyarakat Informatika 16, no. 1 (2025): 104–18. https://doi.org/10.14710/jmasif.16.1.73115.

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Clickbait headlines are widely used in online media to attract readers through exaggerated or misleading titles, potentially leading to user dissatisfaction and information overload. This study proposes a machine learning approach for detecting clickbait in Indonesian news headlines using classical classification models and ensemble learning. The dataset consists of labeled clickbait and non-clickbait headlines in Bahasa Indonesia, which were processed and represented using TF-IDF vectorization. Three base classifiers, Multinomial Naive Bayes, Logistic Regression, and Support Vector Machine, w

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Cui, Su, Yiliang Han, Yifei Duan, Yu Li, Shuaishuai Zhu, and Chaoyue Song. "A Two-Stage Voting-Boosting Technique for Ensemble Learning in Social Network Sentiment Classification." Entropy 25, no. 4 (2023): 555. http://dx.doi.org/10.3390/e25040555.

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In recent years, social network sentiment classification has been extensively researched and applied in various fields, such as opinion monitoring, market analysis, and commodity feedback. The ensemble approach has achieved remarkable results in sentiment classification tasks due to its superior performance. The primary reason behind the success of ensemble methods is the enhanced diversity of the base classifiers. The boosting method employs a sequential ensemble structure to construct diverse data while also utilizing erroneous data by assigning higher weights to misclassified samples in the

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Anderson, Connor J., Daniel Heins, Keith C. Pelletier, and Joseph F. Knight. "Using Voting-Based Ensemble Classifiers to Map Invasive Phragmites australis." Remote Sensing 15, no. 14 (2023): 3511. http://dx.doi.org/10.3390/rs15143511.

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Machine learning is frequently combined with imagery acquired from uncrewed aircraft systems (UASs) to detect invasive plants. Having prior knowledge of which machine learning algorithm will produce the most accurate results is difficult. This study examines the efficacy of a voting-based ensemble classifier to identify invasive Phragmites australis from three-band (red, green, blue; RGB) and five-band (red, green, blue, red edge, near-infrared; multispectral; MS) UAS imagery acquired over multiple Minnesota wetlands. A Random Forest, histogram-based gradient-boosting classification tree, and

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Madhunitha, Rodda. "Fake News Detection Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 7 (2024): 345–51. http://dx.doi.org/10.22214/ijraset.2024.63570.

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Abstract: The pervasive spread of misinformation in today's digital environment presents a daunting problem, influencing public opinion and possibly causing unfavorable social consequences. This paper conducts a thorough investigation into the effectiveness includes a broad variety of false information in text, images, and video formats. Our study carefully assesses SVM, random forest, MLP, and naive Bayes classifiers using a large and painstakingly selected dataset. We provide a classifier of voting to improve accuracy and consistency by integrating the forecasts from these several models. Vo

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Radheshyam Acholiya. "Deep Learning based Glaucoma Classification Performance Evaluation of Stacking Boosting and CNN Ensembles." Panamerican Mathematical Journal 35, no. 3s (2025): 207–29. https://doi.org/10.52783/pmj.v35.i3s.3888.

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Glaucoma remains a major global health concern, leading to irreversible blindness if not detected early. Recent advancements in deep learning have enabled automated glaucoma classification using retinal imaging techniques. This study evaluates four ensemble-based models Stacking (CNN, Transformer, XGBoost), Boosting (CNN Features + LightGBM), Voting CNN Ensemble (EfficientNet, ResNet, DenseNet), and Bagging (Random Forest with CNN Features) to determine the most effective approach for glaucoma detection. Performance analysis based on accuracy, precision, recall, F1-score, and AUC-ROC demonstra

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