Summary of Open Set Recognition For Random Forest, by Guanchao Feng et al.
Open Set Recognition for Random Forest
by Guanchao Feng, Dhruv Desai, Stefano Pasquali, Dhagash Mehta
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 This paper proposes a novel approach to enable open-set recognition capability for random forest classifiers, allowing them to identify samples from unknown classes when deployed in real-world scenarios. The current random forest framework typically operates under the closed-set assumption and is not designed to handle novel classes. To address this limitation, the proposed method incorporates distance metric learning and distance-based open-set recognition techniques. The approach is evaluated on both synthetic and real-world datasets, demonstrating superior performance compared to state-of-the-art distance-based open-set recognition methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a machine to recognize different types of animals or objects. In many cases, it’s hard to collect examples for every single type because new ones might be discovered later. This paper shows how to make the machine better at recognizing known types and also identifying new, unknown types. They do this by improving the way random forest, a popular learning method, handles open-set recognition. The results are impressive and show that their approach works well on both fake and real-world data. |
Keywords
» Artificial intelligence » Random forest
