Methods for Class-Imbalanced Learning with Support Vector Machines: A Review and an Empirical Evaluation
<|reference_start|>Methods for Class-Imbalanced Learning with Support Vector Machines: A Review and an Empirical Evaluation: 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.<|reference_end|>
@article{rezvani2024methods,
    title={Methods for Class-Imbalanced Learning with Support Vector Machines: A
  Review and an Empirical Evaluation},
    author={Salim Rezvani and Farhad Pourpanah and Chee Peng Lim and Q. M.
  Jonathan Wu},
    journal={arXiv preprint arXiv:2406.03398},
    year={2024},
    doi={10.1007/s00500-024-09931-5},
    archivePrefix={arXiv},
    eprint={2406.03398},
    primaryClass={cs.LG}
}