Summary of Confidence Calibration Of Classifiers with Many Classes, by Adrien Lecoz et al.
Confidence Calibration of Classifiers with Many Classes
by Adrien LeCoz, Stéphane Herbin, Faouzi Adjed
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents an innovative approach to confidence calibration in multiclass classification models based on neural networks. By transforming the problem into calibrating a single surrogate binary classifier, the authors enable the efficient use of standard calibration methods, which is particularly important for problems with many classes. The proposed method is evaluated on various neural networks used for image and text classification, demonstrating significant enhancements over existing calibration approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper addresses a common challenge in neural network-based classification models: the maximum predicted class probability often fails to accurately predict the confidence of making a correct prediction. To overcome this limitation, the authors introduce a novel approach that transforms the multiclass problem into a single binary classifier calibration task. This allows for more efficient use of standard calibration methods, which is essential for problems with many classes. |
Keywords
» Artificial intelligence » Classification » Neural network » Probability » Text classification
