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

TL;DR

This paper reviews and empirically evaluates SVM-based methods for class-imbalanced learning, categorizing models into re-sampling, algorithmic, and fusion approaches, and compares their performance and computational efficiency.

Contribution

It introduces a hierarchical categorization of SVM-based models for class imbalance and provides empirical comparisons across various methods and datasets.

Findings

Fusion methods generally outperform others but are more computationally intensive.

Algorithmic methods are faster due to no data pre-processing.

Performance varies with imbalance ratio and method type.

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.