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

arXiv: https://arxiv.org/abs/2406.03398 · HTML: https://mlsanity.com/paper/2406.03398/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 133 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 3 of 3)

- [Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data](https://mlsanity.com/paper/1508.03422/) (2015)
- [A Review of Generalized Zero-Shot Learning Methods](https://mlsanity.com/paper/2011.08641/) (2020)
- [Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification](https://mlsanity.com/paper/1911.01658/) (2019)

## Built on by (most cited first, 3 of 3)

- [Multi-Class Imbalanced Learning with Support Vector Machines via Differential Evolution](https://mlsanity.com/paper/2502.14597/) (2025)
- [Class Confidence Aware Reweighting for Long Tailed Learning](https://mlsanity.com/paper/2601.15924/) (2026)
- [A Direct Classification Approach for Reliable Wind Ramp Event Forecasting under Severe Class Imbalance](https://mlsanity.com/paper/2603.22326/) (2026)

## Abstract (arXiv)

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.