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arXiv · 2109.13449

When in Doubt: Improving Classification Performance with Alternating Normalization

Menglin Jia·Austin Reiter·Ser-Nam Lim·Yoav Artzi·Claire Cardie
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Abstract

We introduce Classification with Alternating Normalization (CAN), a non-parametric post-processing step for classification. CAN improves classification accuracy for challenging examples by re-adjusting their predicted class probability distribution using the predicted class distributions of high-confidence validation examples. CAN is easily applicable to any probabilistic classifier, with minimal computation overhead. We analyze the properties of CAN using simulated experiments, and empirically demonstrate its effectiveness across a diverse set of classification tasks.

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Menglin Jia, Austin Reiter, Ser-Nam Lim, Yoav Artzi, Claire Cardie. 2021-09-28. When in Doubt: Improving Classification Performance with Alternating Normalization. https://arxiv.org/abs/2109.13449

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