Methods for Class-Imbalanced Learning with Support Vector Machines: A Review and an Empirical Evaluation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914881137016832 |
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| author | Rezvani, Salim Pourpanah, Farhad Lim, Chee Peng Wu, Q. M. Jonathan |
| author_facet | Rezvani, Salim Pourpanah, Farhad Lim, Chee Peng Wu, Q. M. Jonathan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03398 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Methods for Class-Imbalanced Learning with Support Vector Machines: A Review and an Empirical Evaluation Rezvani, Salim Pourpanah, Farhad Lim, Chee Peng Wu, Q. M. Jonathan Machine Learning 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. |
| title | Methods for Class-Imbalanced Learning with Support Vector Machines: A Review and an Empirical Evaluation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.03398 |