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Journal articles on the topic 'Stacking classifiers ensemble'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Kasthuriarachchi, K. T. Sanvitha, and Sidath R. Liyanage. "Three-Layer Stacked Generalization Architecture With Simulated Annealing for Optimum Results in Data Mining." International Journal of Artificial Intelligence and Machine Learning 11, no. 2 (2021): 1–27. http://dx.doi.org/10.4018/ijaiml.20210701.oa10.

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The combination of different machine learning models to a single prediction model usually improves the performance of the data analysis. Stacking ensembles are one of such approaches to build a high performance classifier that can be applied to various contexts of data mining. This study proposes an enhanced stacking ensemble by collating a few machine learning algorithms with two layered meta classifications to address the limitations of existing stacking architecture to utilize Simulated Annealing Algorithm to optimize the classifier configuration in order to reach the best prediction accura

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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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Li, Gang, Mengdi Shen, Meixuan Li, and Jingyi Cheng. "Personal Credit Default Discrimination Model Based on Super Learner Ensemble." Mathematical Problems in Engineering 2021 (March 31, 2021): 1–16. http://dx.doi.org/10.1155/2021/5586120.

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Assessing the default of customers is an essential basis for personal credit issuance. This paper considers developing a personal credit default discrimination model based on Super Learner heterogeneous ensemble to improve the accuracy and robustness of default discrimination. First, we select six kinds of single classifiers such as logistic regression, SVM, and three kinds of homogeneous ensemble classifiers such as random forest to build a base classifier candidate library for Super Learner. Then, we use the ten-fold cross-validation method to exercise the base classifier to improve the base

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C, J. Anil Kumar, K. Raghavendra B, and Raghavendra S. "A Credit Scoring Heterogeneous Ensemble Model Using Stacking and Voting." Indian Journal of Science and Technology 15, no. 7 (2022): 300–308. https://doi.org/10.17485/IJST/v15i7.1715.

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Abstract <strong>Background/Objectives:</strong> Recent studies emphasized on using ensemble models over single ones to solve credit scoring problems. The objective of this study is to build a heterogeneous ensemble classifier model with an improved classification accuracy. <strong>Methods:</strong> This study focuses on developing a heterogeneous ensemble classifier using Logistic Regression, K-nearest neighbor, Decision tree, Random Forest, Naïve Base and Support vector machine as base classifiers and Random Forest, Logistic Regression and Support vector machine as meta-c

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A, Emima, and I. George Amalarethinam D. "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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Abstract <strong>Objectives:</strong> To create a stable student performance prediction model utilizing ensemble learning methods. <strong>Methods:</strong> 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

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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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Bharti Chugh, Preeti Garg, and Karnika Dwivedi. "A Comprehensive Ensemble Approach Using Blending and Stacking for Credit Card Fraud Detection." International Journal on Smart & Sustainable Intelligent Computing 1, no. 1 (2024): 19–33. https://doi.org/10.63503/j.ijssic.2024.11.

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Detection of credit card fraud transactions is a severe problem, which requires analyzing large volumes of transaction data to identify fraud patterns. It re-quires finding, which transactions are fraudulent out of millions of daily transactions. As the amount of data is increasing, it is now difficult for an indi-vidual to detect meaningful patterns from transaction data, often character-ized by many samples, many dimensions, and online updates. As a result, there is a need for the best possible approach using machine learning that auto-mates the process of identifying fraudulent patterns fro

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Aljero, Mona Khalifa A., and Nazife Dimililer. "A Novel Stacked Ensemble for Hate Speech Recognition." Applied Sciences 11, no. 24 (2021): 11684. http://dx.doi.org/10.3390/app112411684.

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Detecting harmful content or hate speech on social media is a significant challenge due to the high throughput and large volume of content production on these platforms. Identifying hate speech in a timely manner is crucial in preventing its dissemination. We propose a novel stacked ensemble approach for detecting hate speech in English tweets. The proposed architecture employs an ensemble of three classifiers, namely support vector machine (SVM), logistic regression (LR), and XGBoost classifier (XGB), trained using word2vec and universal encoding features. The meta classifier, LR, combines th

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Odeh, Ammar, and Anas Abu Taleb. "Ensemble learning techniques against structured query language injection attacks." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 1004. http://dx.doi.org/10.11591/ijeecs.v35.i2.pp1004-1012.

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Structured query language (SQL) injection threats pose severe risks to web applications, necessitating robust detection measures. This study introduced DSQLIA, employing ensemble learning algorithms-Bagging, Stacking, and AdaBoost classifiers-for SQL injection detection. Results unveiled the bagging classifier's 84% accuracy with perfect precision (100%) but moderate recall (68%). The stacking classifier achieved 85% accuracy, exceptional precision (99%), and balanced memory (72%), yielding an 83% F1-Score. Remarkably, the AdaBoost classifier outperformed, achieving 99% accuracy, high precisio

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Ammar, Odeh Anas Abu Taleb. "Ensemble learning techniques against structured query language injection attacks." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 1004–12. https://doi.org/10.11591/ijeecs.v35.i2.pp1004-1012.

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Structured query language (SQL) injection threats pose severe risks to web applications, necessitating robust detection measures. This study introduced DSQLIA, employing ensemble learning algorithms-Bagging, Stacking, and AdaBoost classifiers-for SQL injection detection. Results unveiled the bagging classifier's 84% accuracy with perfect precision (100%) but moderate recall (68%). The stacking classifier achieved 85% accuracy, exceptional precision (99%), and balanced memory (72%), yielding an 83% F1-Score. Remarkably, the AdaBoost classifier outperformed, achieving 99% accuracy, high precisio

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Shyam, P. "Credit Card Fraud Detection Using Ensemble (Stacking and Voting Classifiers) with Hybrid Techniques." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 6555–65. https://doi.org/10.22214/ijraset.2025.71710.

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Credit card fraud remains a critical challenge in the financial industry due to the highly imbalanced nature of fraud detection datasets and the evolving tactics of fraudsters. This study proposes a robust framework for Credit Card Fraud Detection Using Ensemble (Stacking and Voting Classifiers) with Hybrid Techniques, integrating advanced resampling strategies with ensemble learning to enhance the detection of minority fraud cases.We evaluated various machine learning models combined with hybrid oversampling and undersampling methods, including Simple Minority Oversampling Technique(SMOTE)-To

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Li, Honglei, Ying Jin, Jiliang Zhong, and Ruixue Zhao. "A Fruit Tree Disease Diagnosis Model Based on Stacking Ensemble Learning." Complexity 2021 (September 14, 2021): 1–12. http://dx.doi.org/10.1155/2021/6868592.

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Fruit tree diseases have a great influence on agricultural production. Artificial intelligence technologies have been used to help fruit growers identify fruit tree diseases in a timely and accurate way. In this study, a dataset of 10,000 images of pear black spot, pear rust, apple mosaic, and apple rust was used to develop the diagnosis model. To achieve better performance, we developed three kinds of ensemble learning classifiers and two kinds of deep learning classifiers, validated and tested these five models, and found that the stacking ensemble learning classifier outperformed the other

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Ding, Weimin, and Shengli Wu. "A cross-entropy based stacking method in ensemble learning." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 4677–88. http://dx.doi.org/10.3233/jifs-200600.

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Stacking is one of the major types of ensemble learning techniques in which a set of base classifiers contributes their outputs to the meta-level classifier, and the meta-level classifier combines them so as to produce more accurate classifications. In this paper, we propose a new stacking algorithm that defines the cross-entropy as the loss function for the classification problem. The training process is conducted by using a neural network with the stochastic gradient descent technique. One major characteristic of our method is its treatment of each meta instance as a whole with one optimizat

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Rosly, Rosaida, Mokhairi Makhtar, Mohd Khalid Awang, Mohd Isa Awang, and Mohd Nordin Abdul Rahman. "Analyzing performance of classifiers for medical datasets." International Journal of Engineering & Technology 7, no. 2.15 (2018): 136. http://dx.doi.org/10.14419/ijet.v7i2.15.11370.

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This paper analyses the performance of classification models using single classification and combination of ensemble method, which are Breast Cancer Wisconsin and Hepatitis data sets as training datasets. This paper presents a comparison of different classifiers based on a 10-fold cross validation using a data mining tool. In this experiment, various classifiers are implemented including three popular ensemble methods which are boosting, bagging and stacking for the combination. The result shows that for the classification of the Breast Cancer Wisconsin data set, the single classification of N

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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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Yusuf, Abdulazeez, and Ayuba John. "Classifiers ensemble and synthetic minority oversampling techniques for academic performance prediction." International Journal of Informatics and Communication Technology (IJ-ICT) 8, no. 3 (2019): 122. http://dx.doi.org/10.11591/ijict.v8i3.pp122-127.

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The increasing need for data driven decision making recently has resulted in the application of data mining in various fields including the educational sector which is referred to as educational data mining. The need for improving the performance of data mining models has also been identified as a gap for future researcher. In Nigeria, higher educational institutions collect various students’ data, but these data are rarely used in any decision or policy making to improve the academic performance of students. This research work, attempts to improve the performance of data mining models for pre

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Abdulazeez, Yusuf, and John Ayuba. "Classifiers ensemble and synthetic minority oversampling techniques for academic performance prediction." International Journal of Informatics and Communication Technology (IJ-ICT) 8, no. 3 (2019): 122–27. https://doi.org/10.11591/ijict.v8i3.pp122-127.

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The increasing need for data driven decision making recently has resulted in the application of data mining in various fields including the educational sector which is referred to as educational data mining. The need for improving the performance of data mining models has also been identified as a gap for future researcher. In Nigeria, higher educational institutions collect various students’ data, but these data are rarely used in any decision or policy making to improve the academic performance of students. This research work, attempts to improve the performance of data mining models f

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Waqas Khan, Prince, and Yung-Cheol Byun. "Multi-Fault Detection and Classification of Wind Turbines Using Stacking Classifier." Sensors 22, no. 18 (2022): 6955. http://dx.doi.org/10.3390/s22186955.

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Wind turbines are widely used worldwide to generate clean, renewable energy. The biggest issue with a wind turbine is reducing failures and downtime, which lowers costs associated with operations and maintenance. Wind turbines’ consistency and timely maintenance can enhance their performance and dependability. Still, the traditional routine configuration makes detecting faults of wind turbines difficult. Supervisory control and data acquisition (SCADA) produces reliable and affordable quality data for the health condition of wind turbine operations. For wind power to be sufficiently reliable,

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Chandrasekar, Jayakumar, Surendar Madhawa, and J. Sangeetha. "Data-driven disruption prediction in GOLEM Tokamak using ensemble classifiers." Journal of Intelligent & Fuzzy Systems 39, no. 6 (2020): 8365–76. http://dx.doi.org/10.3233/jifs-189155.

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A robust disruption prediction system is mandatory in a Tokamak control system as the disruption can cause malfunctioning of the plasma-facing components and impair irrecoverable structural damage to the vessel. To mitigate the disruption, in this article, a data-driven based disruption predictor is developed using an ensemble technique. The ensemble algorithm classifies disruptive and non-disruptive discharges in the GOLEM Tokamak system. Ensemble classifiers combine the predictive capacity of several weak learners to produce a single predictive model and are utilized both in supervised and u

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

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

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SVSV Prasad Sanaboina, M Chandra Naik, K Rajiv. "An Advanced Ensemble Framework Employing Grey Wolf Optimization and Feature Selection Techniques for Enhanced Intrusion Detection on Unbalanced NSL-KDD Data." Communications on Applied Nonlinear Analysis 32, no. 3 (2025): 865–78. https://doi.org/10.52783/cana.v32.4627.

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Intrusion Detection Systems (IDSs) usually face severe issues with imbalanced datasets and the limited ability of a single classifier to generalize well. This research proposes a sophisticated ensemble method combining cutting-edge ensemble learning techniques with Grey Wolf Optimization (GWO), a recent metaheuristic optimization algorithm, and appropriate feature selection methods to significantly improve the accuracy of IDS. The framework is validated via the NSL-KDD dataset, proving that the stacking and voting ensemble methods proposed outperform stand-alone classifiers by a great margin.

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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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Ahmadi, Hossein, and Luca Mesin. "Enhancing Motor Imagery Electroencephalography Classification with a Correlation-Optimized Weighted Stacking Ensemble Model." Electronics 13, no. 6 (2024): 1033. http://dx.doi.org/10.3390/electronics13061033.

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In the evolving field of Brain–Computer Interfaces (BCIs), accurately classifying Electroencephalography (EEG) signals for Motor Imagery (MI) tasks is challenging. We introduce the Correlation-Optimized Weighted Stacking Ensemble (COWSE) model, an innovative ensemble learning framework designed to improve MI EEG signal classification. The COWSE model integrates sixteen machine learning classifiers through a weighted stacking approach, optimizing performance by balancing the strengths and weaknesses of each classifier based on error correlation analysis and performance metrics evaluation across

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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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Afolabi, Hassan A., and Abdurazzag A. Aburas. "Statistical performance assessment of supervised machine learning algorithms for intrusion detection system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 266–77. https://doi.org/10.11591/ijai.v13.i1.pp266-277.

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Several studies have shown that an ensemble classifier's effectiveness is directly correlated with the diversity of its members. However, the algorithms used to build the base learners are one of the issues encountered when using a stacking ensemble. Given the number of options, choosing the best ones might be challenging. In this study, we selected some of the most extensively applied supervised machine learning algorithms and performed a performance evaluation in terms of well-known metrics and validation methods using two internet of things (IoT) intrusion detection datasets, namely network

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Andronati, Oleksandr K., Olena O. Arsirii, Anatoly O. Nikolenko, and Kunditv I. Oleg. "A method of developing ensemble classifiers for recognizing audio data of various nature." Informatics. Culture. Technology 1, no. 1 (2024): 160–66. http://dx.doi.org/10.15276/ict.01.2024.23.

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The work developed a method of building ensemble classifiers for recognizing audio data of various nature. The method is a tentative date and requires the following steps. In the first step, the input datasets are selected, which are transformed and divided into training and test samples, respectively. RAVDESS datasets are chosen for the task of audio emotion recognition, music genre recognition is performed on the GTZAN dataset. In the second step, the following seven classifiers were created and investigated as elements of ensemble classifiers: K Nearest Neighbors, Support Vector Machine, Ra

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Rehman, Amjad, Teg Alam, Muhammad Mujahid, Faten S. Alamri, Bayan Al Ghofaily, and Tanzila Saba. "RDET stacking classifier: a novel machine learning based approach for stroke prediction using imbalance data." PeerJ Computer Science 9 (November 21, 2023): e1684. http://dx.doi.org/10.7717/peerj-cs.1684.

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The main cause of stroke is the unexpected blockage of blood flow to the brain. The brain cells die if blood is not supplied to them, resulting in body disability. The timely identification of medical conditions ensures patients receive the necessary treatments and assistance. This early diagnosis plays a crucial role in managing symptoms effectively and enhancing the overall quality of life for individuals affected by the stroke. The research proposed an ensemble machine learning (ML) model that predicts brain stroke while reducing parameters and computational complexity. The dataset was obta

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Tan, Yan Lin, Ying Han Pang, Shih Yin Ooi, Wee How Khoh, and Fu San Hiew. "Stacking Ensemble Approach for Churn Prediction: Integrating CNN and Machine Learning Models with CatBoost Meta-Learner." Journal of Engineering Technology and Applied Physics 5, no. 2 (2023): 99–107. http://dx.doi.org/10.33093/jetap.2023.5.2.12.

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n the telecom industry, predicting customer churn is crucial for improving customer retention. In literature, the use of single classifiers is predominantly focused. Customer data is complex data due to class imbalance and contain multiple factors that exhibit nonlinear dependencies. In these complex scenarios, single classifiers may be unable to fully utilize the available information to capture the underlying interactions effectively. In contrast, ensemble learning that combines various base classifiers empowers a more thorough data analysis, leading to improved prediction performance. In th

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Chatterjee, Subhajit, and Yung-Cheol Byun. "EEG-Based Emotion Classification Using Stacking Ensemble Approach." Sensors 22, no. 21 (2022): 8550. http://dx.doi.org/10.3390/s22218550.

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Rapid advancements in the medical field have drawn much attention to automatic emotion classification from EEG data. People’s emotional states are crucial factors in how they behave and interact physiologically. The diagnosis of patients’ mental disorders is one potential medical use. When feeling well, people work and communicate more effectively. Negative emotions can be detrimental to both physical and mental health. Many earlier studies that investigated the use of the electroencephalogram (EEG) for emotion classification have focused on collecting data from the whole brain because of the

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Imangaliyev, Sultan, Jörg Schlötterer, Folker Meyer, and Christin Seifert. "Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data." Diagnostics 12, no. 10 (2022): 2514. http://dx.doi.org/10.3390/diagnostics12102514.

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Most of the microbiome studies suggest that using ensemble models such as Random Forest results in best predictive power. In this study, we empirically evaluate a more powerful ensemble learning algorithm, multi-view stacked generalization, on pediatric inflammatory bowel disease and adult colorectal cancer patients’ cohorts. We aim to check whether stacking would lead to better results compared to using a single best machine learning algorithm. Stacking achieves the best test set Average Precision (AP) on inflammatory bowel disease dataset reaching AP = 0.69, outperforming both the best base

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Malmasi, Shervin, and Mark Dras. "Native Language Identification With Classifier Stacking and Ensembles." Computational Linguistics 44, no. 3 (2018): 403–46. http://dx.doi.org/10.1162/coli_a_00323.

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Ensemble methods using multiple classifiers have proven to be among the most successful approaches for the task of Native Language Identification (NLI), achieving the current state of the art. However, a systematic examination of ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble architectures such as classifier stacking have not been closely evaluated. We present a set of experiments using three ensemble-based models, testing each with multiple configurations and algorithms. This includes a rigorous application of meta-classification models for NLI, achieving stat

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Qiu, Ruinan, Yongfeng Yin, Qingran Su, and Tianyi Guan. "Func-Bagging: An Ensemble Learning Strategy for Improving the Performance of Heterogeneous Anomaly Detection Models." Applied Sciences 15, no. 2 (2025): 905. https://doi.org/10.3390/app15020905.

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In the field of ensemble learning, bagging and stacking are two widely used ensemble strategies. Bagging enhances model robustness through repeated sampling and weighted averaging of homogeneous classifiers, while stacking improves classification performance by integrating multiple models using meta-learning strategies, taking advantage of the diversity of heterogeneous classifiers. However, the fixed weight distribution strategy in traditional bagging methods often has limitations when handling complex or imbalanced datasets. This paper combines the concept of heterogeneous classifier integra

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Kharismadhany, Ekky, Maretha Ruswiansari, and Tri Harsono. "Brute-force Detection Using Ensemble Classification." INTEK: Jurnal Penelitian 9, no. 2 (2023): 98. http://dx.doi.org/10.31963/intek.v9i2.3550.

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Traditional brute-force is a dictionary-based attack that tries to unlock an authentication process in service. This type of brute force can be applied in web and SSH services, and brute-force XSS injects JavaScript code. In this paper, we explore four types of ensemble classifiers using CIC-CSE-IDS 2018 to determine which yields the highest accuracy, recall, precision, and F1 in detecting three types of brute force. The first step of the research is to normalise the dataset with the tanH operator. The second step is to train the single classifier to determine three types of single classifiers

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Galchonkov, Oleg, Oleksii Baranov, Mykola Babych, Varvara Kuvaieva, and Yuliia Babych. "Improving the quality of object classification in images by ensemble classifiers with stacking." Eastern-European Journal of Enterprise Technologies 3, no. 9 (123) (2023): 70–77. http://dx.doi.org/10.15587/1729-4061.2023.279372.

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The object of research is the process of classifying objects in images. The quality of classification refers to the ratio of correctly recognized objects to the number of images. One of the options for improving the quality of classification is to increase the depth of neural networks used. The main difficulties along the way are the difficulty of training such neural networks and a large amount of computing that makes it difficult to use them on conventional computers in real time. An alternative way to improve the quality of classification is to increase the width of the neural networks used

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Afolabi, Hassan A., and Aburas A. Abdurazzag. "Statistical performance assessment of supervised machine learning algorithms for intrusion detection system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 266. http://dx.doi.org/10.11591/ijai.v13.i1.pp266-277.

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<span lang="EN-US">Several studies have shown that an ensemble classifier's effectiveness is directly correlated with the diversity of its members. However, the algorithms used to build the base learners are one of the issues encountered when using a stacking ensemble. Given the number of options, choosing the best ones might be challenging. In this study, we selected some of the most extensively applied supervised machine learning algorithms and performed a performance evaluation in terms of well-known metrics and validation methods using two internet of things (IoT) intrusion detection

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Paul, Arunya, Tejaswini Kar, Sasmita Pahadsingh, Priya Chandan Satpathy, and Biswaranjan Behera. "Performance Comparison of different Disease Detection using Stacked Ensemble Learning Model." March 2024 6, no. 1 (2024): 26–39. http://dx.doi.org/10.36548/jscp.2024.1.003.

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Malignancy risks and genetic disorders have long been challenging due to procedures that lack precision and predictability, thereby complicating the precise identification of diseases and their root causes. Machine learning classifiers have emerged as more suitable and effective tools. Various machine learning classifiers have been utilized to examine different genetic disorders, and the results from these classifiers have been further compared to determine their superiority. In this study, a variety of classifiers, including the SVM, KNN, decision tree, random forest, and logistic regression

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Soleymanzadeh, Raha, Mustafa Aljasim, Muhammad Waseem Qadeer, and Rasha Kashef. "Cyberattack and Fraud Detection Using Ensemble Stacking." AI 3, no. 1 (2022): 22–36. http://dx.doi.org/10.3390/ai3010002.

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Smart devices are used in the era of the Internet of Things (IoT) to provide efficient and reliable access to services. IoT technology can recognize comprehensive information, reliably deliver information, and intelligently process that information. Modern industrial systems have become increasingly dependent on data networks, control systems, and sensors. The number of IoT devices and the protocols they use has increased, which has led to an increase in attacks. Global operations can be disrupted, and substantial economic losses can be incurred due to these attacks. Cyberattacks have been det

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Alkhammash, Eman H., Myriam Hadjouni, and Ahmed M. Elshewey. "A Hybrid Ensemble Stacking Model for Gender Voice Recognition Approach." Electronics 11, no. 11 (2022): 1750. http://dx.doi.org/10.3390/electronics11111750.

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Gender recognition by voice is a vital research subject in speech processing and acoustics, as human voices have many remarkable characteristics. Voice recognition is beneficial in a variety of applications, including mobile health care systems, interactive systems, crime analysis, and recognition systems. Several algorithms for voice recognition have been developed, but there is still potential for development in terms of the system’s accuracy and efficiency. Recent research has focused on combining ensemble learning with a variety of machine learning models in order to create more accurate c

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Ghorpade, Smita Jaywantrao, Ratna Sadashiv Chaudhari, and Seema Sajanrao Patil. "Enhancement of Imbalance Data Classification with Boosting Methods: An Experiment." ECS Transactions 107, no. 1 (2022): 15923–34. http://dx.doi.org/10.1149/10701.15923ecst.

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The idea of boosting emanates from the area of machine learning. It is a challenging task for imbalance data set to have appropriate distribution of data samples in each class by machine learning algorithm. To deal with this problem, ensemble learning method is one of the popular approaches. Ensemble methods integrate several learning algorithms, which gives better predictive performance as compared to any of the basic learning algorithms alone. Based on this research, a question is formulated. The null hypothesis is stated as “There is no significant difference between single classifier and c

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Yücesoy, Ergün. "Automatic Age and Gender Recognition Using Ensemble Learning." Applied Sciences 14, no. 16 (2024): 6868. http://dx.doi.org/10.3390/app14166868.

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The use of speech-based recognition technologies in human–computer interactions is increasing daily. Age and gender recognition, one of these technologies, is a popular research topic used directly or indirectly in many applications. In this research, a new age and gender recognition approach based on the ensemble of different machine learning algorithms is proposed. In the study, five different classifiers, namely KNN, SVM, LR, RF, and E-TREE, are used as base-level classifiers and the majority voting and stacking methods are used to create the ensemble models. First, using MFCC features, fiv

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Gaikwad, D. P., Vismita Nagrale, and M. P. Bauskar. "Ensemble of Learner for Network Intrusion Detection System." Journal of Network Security Computer Networks 9, no. 1 (2023): 25–34. http://dx.doi.org/10.46610/jonscn.2023.v09i01.004.

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The uses of the internet have improved drastically for online communication and working from home. Data sharing and integration of global information bring network security risks. To protect private data and information, network security is becoming a very important research topic. An intrusion detection system is generally used as safe operational tool. It excellently detects and prevents intruders in a network by issuing a warning before the attack is launched in a network. An ensemble technique is extensively used to employ intrusion detection systems. In this paper, the stacking method of

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Setiawan, Yahya, Jondri Jondri, and Widi Astuti. "Twitter Sentiment Analysis on Online Transportation in Indonesia Using Ensemble Stacking." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 3 (2022): 1452. http://dx.doi.org/10.30865/mib.v6i3.4359.

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Online transportation is a transportation innovation that has emerged along with the development of online-based applications that provide many features and conveniences. In its development, many users wrote their responses to the application on social media such as twitter. Many opinions and responses are directly conveyed by users of online transportation modes to their official accounts. The responses given by these users are very large and can be used as sentiment analysis on online transportation. However, the analysis process cannot be done manually. Therefore, we need a system that can

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Davoulos, George, Iro Lalakou, and Ioannis Hatzilygeroudis. "From Single to Deep Learning and Hybrid Ensemble Models for Recognition of Dog Motion States." Electronics 14, no. 10 (2025): 1924. https://doi.org/10.3390/electronics14101924.

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Dog activities recognition, especially dog motion status recognition, is an active research area. Although several machine learning and deep learning approaches have been used for dog motion states recognition, the use of ensemble learning methods is rather missing, as well as a comparison with deep learning ones. This paper focuses on the use of deep learning neural networks and ensemble classifiers in recognizing dog motion states and their comparison. A dataset from the Kaggle database, which includes measures by accelerometer and gyroscope and concerns seven dog motion states (galloping, s

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Jaiyeoba, Oluwayemisi, Emeka Ogbuju, Owolabi Temitope Yomi, and Francisca Oladipo. "Development of a Model to Classify Skin Diseases using Stacking Ensemble Machine Learning Techniques." Journal of Computing Theories and Applications 2, no. 1 (2024): 22–38. http://dx.doi.org/10.62411/jcta.10488.

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Skin diseases are highly prevalent and transmissible. It has been one of the major health problems that most people face. The diseases are dangerous to the skin and tend to spread over time. A patient can be cured of these skin diseases if they are detected on time and treated early. However, it is difficult to identify these diseases and provide the right medications. This study's research objectives involve developing an ensemble machine learning based model for classifying Erythemato-Squamous Diseases (ESD). The ensemble techniques combine five different classifiers, Naïve Bayes, Support Ve

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Khan, Asfandyar, Abdullah Khan, Muhammad Muntazir Khan, Kamran Farid, Muhammad Mansoor Alam, and Mazliham Bin Mohd Su’ud. "Cardiovascular and Diabetes Diseases Classification Using Ensemble Stacking Classifiers with SVM as a Meta Classifier." Diagnostics 12, no. 11 (2022): 2595. http://dx.doi.org/10.3390/diagnostics12112595.

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Cardiovascular disease includes coronary artery diseases (CAD), which include angina and myocardial infarction (commonly known as a heart attack), and coronary heart diseases (CHD), which are marked by the buildup of a waxy material called plaque inside the coronary arteries. Heart attacks are still the main cause of death worldwide, and if not treated right they have the potential to cause major health problems, such as diabetes. If ignored, diabetes can result in a variety of health problems, including heart disease, stroke, blindness, and kidney failure. Machine learning methods can be used

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Priya, M., and M. Rajeshwari. "A Data Mining Approach for Intrusion Detection in a Computer Network." Asian Journal of Computer Science and Technology 8, S1 (2019): 94–97. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1942.

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As activities being done on the internet keep expanding every day due to the fact that we are in the era of the information age, securing sensitive and crucial data on computer networks against malicious attacks tends to be a challenging issue. Designing effective Intrusion Detection Systems (IDSs) with maximized accuracy and low rate of false alarms is an imperative need in the world of cyber-attacks. This work was designed to employ an ensemble data mining technique for improving IDSs by carrying out some experiments using the KDD 99 intrusion dataset. Dataset was fragmented into five, repre

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Song, Shijie, Xiaohong Wu, Mingyu Li, and Bin Wu. "Identifying the Geographical Origin of Wolfberry Using Near-Infrared Spectroscopy and Stacking-Orthogonal Linear Discriminant Analysis." Foods 14, no. 10 (2025): 1684. https://doi.org/10.3390/foods14101684.

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The geographical origin identification of wolfberry is key to ensuring its medicinal and edible quality. To accurately identify the geographical origin, the Stacking-Orthogonal Linear Discriminant Analysis (OLDA) algorithm was proposed by combining OLDA with the Stacking ensemble learning framework. In this study, Savitzky–Golay (SG) + Multiplicative Scatter Correction (MSC) served as the optimal preprocessing method. Four classifiers—K-Nearest Neighbors (KNN), Decision Tree, Support Vector Machine (SVM), and Naive Bayes—were used to explore 12 stacked combinations on 400 samples from five reg

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Prakash, V. Jothi, and N. K. Karthikeyan. "Dual-Layer Deep Ensemble Techniques for Classifying Heart Disease." Information Technology and Control 51, no. 1 (2022): 158–79. http://dx.doi.org/10.5755/j01.itc.51.1.30083.

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The prevalence of heart disease is increasing at a rapid rate due to changes in food habits and lifestyle of peopleall over the world. Early prediction and diagnosis of this fatal disease is a highly excruciating task. Nowadays, theensemble learning approaches are preferred owing to their effectiveness in performance when compared to theperformance of a single classification algorithm. In this work, a Dual-Layer Stacking Ensemble (DLSE) techniqueand a Deep Heterogeneous Ensemble (DHE) technique to classify heart disease are proposed. The DLSE uses several heterogeneous classifiers to form an e

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Shi, Feifei, Xiaohong Gao, Runxiang Li, and Hao Zhang. "Ensemble Learning for the Land Cover Classification of Loess Hills in the Eastern Qinghai–Tibet Plateau Using GF-7 Multitemporal Imagery." Remote Sensing 16, no. 14 (2024): 2556. http://dx.doi.org/10.3390/rs16142556.

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The unique geographic environment, diverse ecosystems, and complex landforms of the Qinghai–Tibet Plateau make accurate land cover classification a significant challenge in plateau earth sciences. Given advancements in machine learning and satellite remote sensing technology, this study investigates whether emerging ensemble learning classifiers and submeter-level stereoscopic images can significantly improve land cover classification accuracy in the complex terrain of the Qinghai–Tibet Plateau. This study utilizes multitemporal submeter-level GF-7 stereoscopic images to evaluate the accuracy

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Zhang, Peng, Shougeng Hu, Weidong Li, Chuanrong Zhang, and Peikun Cheng. "Improving Parcel-Level Mapping of Smallholder Crops from VHSR Imagery: An Ensemble Machine-Learning-Based Framework." Remote Sensing 13, no. 11 (2021): 2146. http://dx.doi.org/10.3390/rs13112146.

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Explicit spatial information about crop types on smallholder farms is important for the development of local precision agriculture. However, due to highly fragmented and heterogeneous cropland landscapes, fine-scale mapping of smallholder crops, based on low- and medium-resolution satellite images and relying on a single machine learning (ML) classifier, generally fails to achieve satisfactory performance. This paper develops an ensemble ML-based framework to improve the accuracy of parcel-level smallholder crop mapping from very high spatial resolution (VHSR) images. A typical smallholder agr

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