2024
DOI: 10.36227/techrxiv.171623912.22862601/v1
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A survey on confidence calibration of deep learning under class imbalance data

Jinzong Dong,
Zhaohui Jiang,
Dong Pan
et al.

Abstract: Confidence calibration in classification models, a technique to achieve accurate posterior probability estimation for classification results, is crucial for assessing the likelihood of correct decisions in real-world applications. Class imbalance data, which biases the learning of the model and subsequently skews the posterior probabilities of the model, makes confidence calibration more challenging. Especially for often more important minority classes with high uncertainty, confidence calibration is more comp… Show more

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