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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 12/2019

08-05-2019 | Original Article

A Gaussian mixture model based combined resampling algorithm for classification of imbalanced credit data sets

Authors: Xu Han, Runbang Cui, Yanfei Lan, Yanzhe Kang, Jiang Deng, Ning Jia

Published in: International Journal of Machine Learning and Cybernetics | Issue 12/2019

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Abstract

Credit scoring represents a two-classification problem. Moreover, the data imbalance of the credit data sets, where one class contains a small number of data samples and the other contains a large number of data samples, is an often problem. Therefore, if only a traditional classifier is used to classify the data, the final classification effect will be affected. To improve the classification of the credit data sets, a Gaussian mixture model based combined resampling algorithm is proposed. This resampling approach first determines the number of samples of the majority class and the minority class using a sampling factor. Then, the Gaussian mixture clustering is used for undersampling of the majority of samples, and the synthetic minority oversampling technique is used for the rest of the samples, so an eventual imbalance problem is eliminated. Here we compare several resampling methods commonly used in the analysis of imbalanced credit data sets. The obtained experimental results demonstrate that the proposed method consistently improves classification performances such as F-measure, AUC, G-mean, and so on. In addition, the method has strong robustness for credit data sets.
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Metadata
Title
A Gaussian mixture model based combined resampling algorithm for classification of imbalanced credit data sets
Authors
Xu Han
Runbang Cui
Yanfei Lan
Yanzhe Kang
Jiang Deng
Ning Jia
Publication date
08-05-2019
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 12/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-019-00953-2

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