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Computer Science > Machine Learning

arXiv:2109.13449 (cs)
[Submitted on 28 Sep 2021]

Title:When in Doubt: Improving Classification Performance with Alternating Normalization

Authors: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.
Comments: Findings of EMNLP 2021
Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2109.13449 [cs.LG]
  (or arXiv:2109.13449v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.13449

Submission history

From: Menglin Jia [view email]
[v1] Tue, 28 Sep 2021 02:55:42 UTC (8,398 KB)
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Menglin Jia
Austin Reiter
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Yoav Artzi
Claire Cardie
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