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30 articles for “Class Imbalance”
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Research ArticleOpen accessPhrase in the titleDeep Regularization Mechanism for Combating Class Imbalance Problem in Intrusion Detection System for Defending DDoS Attack in SDNNarender M, Yuvaraju B NIntegration of deep learning into Intrusion Detection Systems (IDS) for Software Defined Networking (SDN) is an emerging field of research. Most of the datasets used to build IDS are highly imbalanced, especially in the case of DDoS attacks, which account for a larger percentage of malicious samples than normal traffic. As a result of a class imbalance, the classification result … …Journal of Computer Science, Vol. 19, Issue 3 · 24 Feb 2023 · 5,659 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleAdaptive Synthetic Oversampling Algorithm for Handling Class Imbalance in Multi-Class Data Stream ClassificationPriya S., Annie UthraConcept drift and class imbalanced data are major challenging processes involved in modern streaming data classification. Particularly, when integrated with difficult factors like the existence of noise, overlapping class distribution, concept drift, and data imbalance can considerably affect the classifier results. In addition, various challenges affect the performance of the existing oversampling schemes such as SMOTE and its derivatives. Regardless … …Journal of Computer Science, Vol. 18, Issue 7 · 26 Jul 2022 · 5,809 accessesView PDFCite
Review ArticleOpen accessPhrase in the titleA Wide Scale Classification of Class Imbalance Problem and its Solutions: A Systematic Literature ReviewGillala Rekha, Amit Kumar Tyagi, V. Krishna ReddyIn today’s world, most of the data (real world) is present in imbalanced form by nature. This is because of not having efficient algorithms to put this data (i.e., generated data by billion of internet- connected devices (IoTs)) in respective format. Imbalanced data poses a great challenge to (both) data mining and machine learning algorithms. The imbalanced dataset consists of … …Journal of Computer Science, Vol. 15, Issue 7 · 3 Jul 2019 · 9,372 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleA Comparative Study of Resampling, Cost-Sensitive, and Ensemble Techniques for Handling Class Imbalance in Indonesian Financial DataGunawan Kurnia, Ditdit Nugeraha UtamaHandling class imbalance is a critical challenge in machine learning applications, particularly in financial domains where minority instances often represent significant anomalies such as fraud or audit risks. Various oversampling and undersampling methods were tested, alongside cost-sensitive adjustments and ensemble models including Random Forest, AdaBoost, Gradient Boosting, and XGBoost. The evaluation, based on 10-fold stratified cross-validation and performance metrics such … …Journal of Computer Science, Vol. 22, Issue 2 · 27 Feb 2026 · 1,874 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleCombining SMOTE and OVA with Deep Learning and Ensemble Classifiers for Multiclass ImbalancedRattanawadee PanthongThe classification of real-world problems alwaysconsists of imbalanced and multiclass datasets. A dataset having unbalanced andmultiple classes will have an impact on the pattern of the classification modeland the classification accuracy, which will be decreased. Hence,oversampling method keeps the class of dataset balanced and avoids theoverfitting problem. The purposes of the study were to handle multiclassimbalanced datasets and to improve … …Journal of Computer Science, Vol. 18, Issue 8 · 23 Aug 2022 · 6,096 accessesView PDFCite
Research ArticleOpen accessIn the keywordsForecasting Corporate Financial Distress Using Explainable Machine Learning: Implications for Risk Management TechnologyKanjana Hinthaw, Warawut NarkbunnumFinancial distress prediction is a critical task in financial risk management, yet it remains challenging due to class imbalance, complex financial structures, and limited model interpretability. This study develops an integrated machine learning framework that combines feature selection, class imbalance handling, threshold optimization, and Explainable Artificial Intelligence (XAI) to improve both predictive performance and decision-making reliability. Using a dataset of … …Journal of Computer Science, Vol. 22, Issue 7 · 22 Jul 2026 · 1,130 accessesView PDFCite
Research ArticleOpen accessIn the keywordsUncertainty-Aware Ensemble Models for Improved Defect Detection in Noisy DataMadhavi Perla, Gadi Lava Raju, A Radha Krishna et al.Software defect prediction plays a crucial role in ensuring software quality and reliability, especially as modern systems become more complex and data rich. This study introduces an uncertainty-aware ensemble learning framework aimed at improving defect classification performance in noisy and imbalanced datasets, particularly those from the PROMISE and NASA KC1 repositories. The proposed model integrates multiple classifiers in a multi-learner … …Journal of Computer Science, Vol. 22, Issue 4 · 27 Apr 2026 · 1,542 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Comparative Analysis of Smote and CSSF Techniques for Diabetes Classification Using Imbalanced DataBashar Hamad Aubaidan, Rabiah Abdul Kadir, Mohamad Taha IjabDiabetes, a prevalent chronic metabolic disorder, poses a significant burden on healthcare systems worldwide. Accurate and timely diagnosis is crucial for effective management and complication prevention. Machine learning presents a promising solution but often faces challenges due to class imbalance within datasets, particularly the underrepresentation of diabetic cases. To address this issue, we introduce Cluster-based Synthetic Sample Filtering (CSSF), a … …Journal of Computer Science, Vol. 20, Issue 9 · 22 Jul 2024 · 4,998 accessesView PDFCite
Review ArticleOpen accessIn the keywordsA Survey of Methods for Managing the Classification and Solution of Data Imbalance ProblemKhan Md. Hasib, Md. Sadiq Iqbal, Faisal Muhammad Shah et al.The problem of class imbalance is extensive for focusing on numerous applications in the real world. In such a situation, nearly all of the examples are labeled as one class called majority class, while far fewer examples are labeled as the other class usually, the more important class is called minority. Over the last few years, several types of research … …Journal of Computer Science, Vol. 16, Issue 11 · 13 Nov 2020 · 10,120 accessesView PDFCite
Review ArticleOpen accessIn the keywordsAn Insight into Rare Class Problem: Analysis and Potential SolutionsSatyam Maheshwari, R.C. Jain, R.S. JadonThe class imbalance problem presents an important challenge to the data mining community, in which the number of examples of one class is more than the others. This problem is characterized by a different distribution of cases between all the classes. In this paper, our goal is to study the various challenges of class imbalance problem and provide a comparative … …Journal of Computer Science, Vol. 14, Issue 6 · 9 May 2018 · 8,387 accessesView PDFCite