Summary of Ultra-imbalanced Classification Guided by Statistical Information, By Yin Jin et al.
Ultra-imbalanced classification guided by statistical information
by Yin Jin, Ningtao Wang, Ruofan Wu, Pengfei Shi, Xing Fu, Weiqiang Wang
First submitted to arxiv on: 6 Sep 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 The proposed ultra-imbalanced classification (UIC) formulation addresses the challenge of imbalanced learning in real-world applications like fraud detection. Unlike previous works focusing on minority classes with few samples, UIC handles cases where the minority class contains abundant samples. The paper introduces a novel loss function called Tunable Boosting Loss, which is provably resistant to data imbalance under UIC and empirically efficient on both public and industrial datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores ways to improve classification accuracy when dealing with imbalanced data in real-world applications like fraud detection. Instead of focusing on minority classes with few samples, the study looks at cases where the minority class has many samples. The authors propose a new approach called ultra-imbalanced classification (UIC) and develop a new loss function that can handle this type of imbalance. They test their method on both public and industrial datasets and show that it is effective in improving accuracy. |
Keywords
» Artificial intelligence » Boosting » Classification » Loss function
