In this paper, we propose to address the class imbalance problem by applying an SVM-based ensemble framework that provides the ability to control the trade-off ...
In this research, combine sampling was done by combining SMOTE and Tomek links techniques along with SVM as the binary classification method.
ABSTRACT. The class imbalance problem in classification scenarios is considered to be one of the main issues that limits the per-.
Sep 18, 2018 · Class imbalance can be an intrinsic property in datasets or it can occur due to limitations in obtaining the data attributed to cost, ...
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Sep 15, 2023 · The proposed method combines α-cut fuzzy number method for screening representative examples of majority class and MMTD method for creating new ...
Apr 18, 2021 · Class imbalance is considered a common issue that causes problems for machine learning models, especially SVM.
Nov 17, 2022 · Many researchers combine svm classifiers with optimization algorithms that solve the feature selection problem. The article [78] used nsga-iii ...
Some basic ideas on the approach to the data level by using sampling-based approaches to handle this classification issue are under sampling and oversampling.
In this paper, we propose to address the class imbalance problem by applying an SVM-based ensemble framework that provides the ability to control the trade-off ...
May 23, 2020 · In this article, we propose the OCEIS classifier (One-Class support vector machine classifier Ensemble for Imbalanced data Stream).