When in Doubt: Improving Classification Performance with Alternating Normalization
Menglin Jia, Austin Reiter, Ser-Nam Lim, Yoav Artzi, Claire Cardie · 2021
We introduce Classification with Alternating Normalization (CAN), a non-parametric postprocessing 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 1 .