Summary of Methods For Class-imbalanced Learning with Support Vector Machines: a Review and An Empirical Evaluation, by Salim Rezvani and Farhad Pourpanah and Chee Peng Lim and Q. M. Jonathan Wu
Methods for Class-Imbalanced Learning with Support Vector Machines: A Review and an Empirical Evaluation
by Salim Rezvani, Farhad Pourpanah, Chee Peng Lim, Q. M. Jonathan Wu
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper reviews methods for class-imbalanced learning using Support Vector Machines (SVM) and its variants. The authors explain the structure of SVM and its inefficiency in handling imbalanced datasets, then categorize SVM-based models into re-sampling, algorithmic, and fusion methods based on their approaches to class-imbalanced learning. Empirical evaluations comparing performances of representative models from each category are conducted using benchmark imbalanced datasets with varying ratios. The study finds that while algorithmic methods are faster due to no data preprocessing requirements, fusion methods, combining re-sampling and algorithmic approaches, generally perform best but at a higher computational cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make computers learn better when some classes have more examples than others. The authors talk about a type of computer program called Support Vector Machines (SVM) that can be used for this task. They divide SVM into three types: re-sampling, algorithmic, and fusion methods, each with its own way of dealing with imbalanced data. The paper then compares the performance of these different approaches using real-world datasets. Overall, the study finds that a combination of the three approaches works best, but takes more time to do so. |
