Summary of Otlp: Output Thresholding Using Mixed Integer Linear Programming, by Baran Koseoglu et al.
OTLP: Output Thresholding Using Mixed Integer Linear Programming
by Baran Koseoglu, Luca Traverso, Mohammed Topiwalla, Egor Kraev, Zoltan Szopory
First submitted to arxiv on: 18 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 proposed OTLP (Output Thresholding LP) is a model-agnostic thresholding framework that uses mixed integer linear programming to search for the best threshold during inference in high imbalance classification problems. This framework can support different objective functions and constraint sets, making it suitable for various real-world applications where theoretical thresholding techniques may not be effective. The authors evaluate the performance of OTLP using the Credit Card Fraud Detection Dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to find the best threshold for machine learning models that produce probability estimates. This is especially helpful when there are more examples of one class than another, which can make it harder for the model to perform well. The method, called OTLP, uses math to search for the best threshold and can be used with different types of problems and constraints. It’s useful in real-world applications where machine learning models are used. |
Keywords
» Artificial intelligence » Classification » Inference » Machine learning » Probability
