Improvement of Batch Normalization in Imbalanced Data

Muneki Yasuda; Seishirou Ueno

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arXiv · 1911.10687

Improvement of Batch Normalization in Imbalanced Data

Muneki Yasuda·Seishirou Ueno

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Abstract

In this study, we consider classification problems based on neural networks in data-imbalanced environment. Learning from an imbalanced data set is one of the most important and practical problems in the field of machine learning. A weighted loss function based on cost-sensitive approach is a well-known effective method for imbalanced data sets. We consider a combination of weighted loss function and batch normalization (BN) in this study. BN is a powerful standard technique in the recent developments in deep learning. A simple combination of both methods leads to a size-mismatch problem due to a mismatch between interpretations of effective size of data set in both methods. We propose a simple modification to BN to correct the size-mismatch and demonstrate that this modified BN is effective in data-imbalanced environment.

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BibTeX RIS

Muneki Yasuda, Seishirou Ueno. 2019-11-25. Improvement of Batch Normalization in Imbalanced Data. https://arxiv.org/abs/1911.10687

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