Summary of Minimum Enclosing Ball Synthetic Minority Oversampling Technique From a Geometric Perspective, by Yi-yang Shangguan et al.
Minimum Enclosing Ball Synthetic Minority Oversampling Technique from a Geometric Perspective
by Yi-Yang Shangguan, Shi-Shun Chen, Xiao-Yang Li
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Geometry (cs.CG)
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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 tackles the class imbalance problem in machine learning, a common issue where one class has significantly more samples than others. The synthetic minority oversampling technique (SMOTE) is widely used to address this issue, but it has limitations. To overcome these shortcomings, the authors propose a new method called Minimum Enclosing Ball SMOTE (MEB-SMOTE). This method uses geometric concepts to construct a representative point that is suitable for synthesizing new samples. The authors demonstrate the effectiveness of MEB-SMOTE by conducting experiments on 15 real-world imbalanced datasets, showing improved classification performance compared to traditional SMOTE and its variants. The paper contributes to the development of more robust machine learning models for applications like software defect prediction, medical diagnosis, and fraud detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to find a specific type of rock in a huge pile of different rocks. If most of the rocks are one type and only a few are the type you’re looking for, it’s hard to find them all. This is called class imbalance. It happens when there are many more samples from one group than another. The paper talks about how to fix this problem using something called SMOTE (synthetic minority oversampling technique). They introduce a new way of doing SMOTE that uses geometric shapes to create better fake data. This helps the computer learn better and make fewer mistakes. They tested their method on 15 real datasets and it worked well, which is important for things like diagnosing diseases or detecting fraud. |
Keywords
» Artificial intelligence » Classification » Machine learning
