Summary of Improving Online Bagging For Complex Imbalanced Data Stream, by Bartosz Przybyl and Jerzy Stefanowski
Improving Online Bagging for Complex Imbalanced Data Stream
by Bartosz Przybyl, Jerzy Stefanowski
First submitted to arxiv on: 4 Oct 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 extensions to resampling online bagging, specifically Neighbourhood Undersampling or Oversampling Online Bagging, aim to improve the performance of popular online classifiers in learning from concept drifting and imbalanced data streams. By considering local difficulty factors such as minority class decomposition into sub-concepts and the presence of unsafe examples (borderline or rare ones), these extensions outperform earlier variants of online bagging resampling ensembles on synthetic complex imbalanced data streams. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The new approach focuses on addressing changes in local imbalance ratios, not just overall global imbalance, to better adapt to concept drift and minority class decomposition. This leads to improved performance for popular online classifiers, making them more effective at learning from challenging data streams. |
Keywords
» Artificial intelligence » Bagging
