Summary of A Mallows-like Criterion For Anomaly Detection with Random Forest Implementation, by Gaoxiang Zhao et al.
A Mallows-like Criterion for Anomaly Detection with Random Forest Implementation
by Gaoxiang Zhao, Lu Wang, Xiaoqiang Wang
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 paper proposes a novel approach to anomaly signal detection by introducing a novel criterion for selecting weights when aggregating multiple models. This framework, based on the model average method, incorporates a focal loss function that addresses extremely imbalanced data classification. The proposed strategy is integrated into the Random Forest algorithm by replacing the conventional voting method. The authors evaluate their method on benchmark datasets across various domains, including network intrusion, and find that it surpasses model averaging with typical loss functions as well as common anomaly detection algorithms in terms of accuracy and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to detect anomalies by combining multiple models together. Instead of using one specific model, the authors suggest choosing weights based on how well each model does at detecting anomalies. This is especially helpful when dealing with very imbalanced data, where most instances are normal but some are anomalous. The authors use this approach in combination with Random Forest algorithm and test it on various datasets. Their results show that their method performs better than other methods in terms of accuracy and robustness. |
Keywords
» Artificial intelligence » Anomaly detection » Classification » Loss function » Random forest
