Methods for Class-Imbalanced Learning with Support Vector Machines: A Review and an Empirical Evaluation

• URL: http://arxiv.org/abs/2406.03398v2
• Date: Wed, 12 Jun 2024 02:37:08 GMT
• Title: Methods for Class-Imbalanced Learning with Support Vector Machines: A Review and an Empirical Evaluation
• Authors: Salim Rezvani, Farhad Pourpanah, Chee Peng Lim, Q. M. Jonathan Wu,
• Abstract summary: We introduce a hierarchical categorization of SVM-based models with respect to class-imbalanced learning. We compare the performances of various representative SVM-based models in each category using benchmark imbalanced data sets. Our findings reveal that while algorithmic methods are less time-consuming owing to no data pre-processing requirements, fusion methods, which combine both re-sampling and algorithmic approaches, generally perform the best.
• Score: 22.12895887111828
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents a review on methods for class-imbalanced learning with the Support Vector Machine (SVM) and its variants. We first explain the structure of SVM and its variants and discuss their inefficiency in learning with class-imbalanced data sets. We introduce a hierarchical categorization of SVM-based models with respect to class-imbalanced learning. Specifically, we categorize SVM-based models into re-sampling, algorithmic, and fusion methods, and discuss the principles of the representative models in each category. In addition, we conduct a series of empirical evaluations to compare the performances of various representative SVM-based models in each category using benchmark imbalanced data sets, ranging from low to high imbalanced ratios. Our findings reveal that while algorithmic methods are less time-consuming owing to no data pre-processing requirements, fusion methods, which combine both re-sampling and algorithmic approaches, generally perform the best, but with a higher computational load. A discussion on research gaps and future research directions is provided.

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