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2018 | OriginalPaper | Buchkapitel

Over-Sampling Algorithm Based on VAE in Imbalanced Classification

verfasst von : Chunkai Zhang, Ying Zhou, Yingyang Chen, Yepeng Deng, Xuan Wang, Lifeng Dong, Haoyu Wei

Erschienen in: Cloud Computing – CLOUD 2018

Verlag: Springer International Publishing

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Abstract

The imbalanced classification problem is a problem that violates the assumption of uniform distribution of samples, classes differ in sample size, sample distribution and misclassification cost. The traditional classifiers tend to ignore the important minority samples because of their rarity. Oversampling, the algorithm uses various methods to increase the minority samples in the training set to increase the recognition rate of them. However, these over-sampling methods are too coarse to improve the classification effect of the minority samples, because they can’t make full use of the information in the original samples, but increase the training time because of adding extra samples. In this paper, we propose to use the distribution information of the minority samples, use the variational auto-encoder to fit the probability distribution function of them without any prior assumption, and reasonably expand the minority class sample set. The experimental results prove the effectiveness of the proposed algorithm.

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Titel: Over-Sampling Algorithm Based on VAE in Imbalanced Classification
verfasst von: Chunkai Zhang
Ying Zhou
Yingyang Chen
Yepeng Deng
Xuan Wang
Lifeng Dong
Haoyu Wei
Verlag: Springer International Publishing
Buch: Cloud Computing – CLOUD 2018
Print ISBN: 978-3-319-94294-0

Electronic ISBN: 978-3-319-94295-7

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-94295-7_23

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