Summary of Evosampling: a Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer For Imbalanced Learning, by Wenbin Pei et al.
EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning
by Wenbin Pei, Ruohao Dai, Bing Xue, Mengjie Zhang, Qiang Zhang, Yiu-Ming Cheung, Shuyin Xia
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 The proposed EvoSampling method addresses class imbalance in machine learning by developing an evolutionary multi-granularity hybrid sampling technique. This approach combines genetic programming with multi-task learning to generate diverse high-quality instances during oversampling, and a granular ball-based undersampling method to remove noise effectively. The authors demonstrate the effectiveness of EvoSampling on 20 imbalanced datasets, showing improved performance for various classification algorithms compared to existing sampling methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper proposes a new way to fix class imbalance in machine learning. Class imbalance happens when one class has much more data than others, which can make models biased towards the majority class. To address this issue, the authors developed a method that uses evolutionary algorithms and multi-task learning to generate high-quality instances of the minority class, while also removing noisy data. They tested their method on 20 datasets and found it outperformed existing methods. |
Keywords
» Artificial intelligence » Classification » Machine learning » Multi task
