Summary of Tree-based Ensemble Learning For Out-of-distribution Detection, by Zhaiming Shen et al.
Tree-based Ensemble Learning for Out-of-distribution Detection
by Zhaiming Shen, Menglun Wang, Guang Cheng, Ming-Jun Lai, Lin Mu, Ruihao Huang, Qi Liu, Hao Zhu
First submitted to arxiv on: 5 May 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 paper proposes a novel approach for detecting out-of-distribution (OoD) samples using tree-based embeddings. The proposed method, called TOOD detection, leverages pairwise hamming distance calculations to determine whether unseen samples have similar distributions as the training data. This is achieved by fitting a tree-based ensemble model through in-distribution training samples and computing their embeddings. The approach is interpretable, robust, efficient, and flexible across various machine learning tasks and datasets. Experimental results demonstrate its superiority over state-of-the-art OoD detection methods on tabular, image, and text data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us figure out if new information is similar to what we already know. It’s like checking if a new book is part of the same series as one you’ve read before. The method uses special trees to understand how different pieces of information are related. This makes it good at identifying when something is unusual or doesn’t fit with what we already know. The approach is easy to understand, works well on lots of different types of data, and can even be used without knowing the answers beforehand. |
Keywords
» Artificial intelligence » Ensemble model » Machine learning
