When in Doubt: Improving Classification Performance with Alternating Normalization

• URL: http://arxiv.org/abs/2109.13449v1
• Date: Tue, 28 Sep 2021 02:55:42 GMT
• Title: When in Doubt: Improving Classification Performance with Alternating Normalization
• Authors: Menglin Jia, Austin Reiter, Ser-Nam Lim, Yoav Artzi and Claire Cardie
• Abstract summary: 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. We empirically demonstrate its effectiveness across a diverse set of classification tasks.
• Score: 57.39356691967766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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