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A clustering and generative adversarial networks-based hybrid approach for imbalanced data classification

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Abstract

Imbalanced data often exists in related fields such as banking, insurance, security and medical care. The imbalanced distribution of data will lead to the deviation of decision-making, making it easy for a small number of samples to be divided incorrectly. Therefore, as a challenging task, imbalanced data classification has attracted extensive research in many disciplines. In this study, we propose a method based on the combination of clustering and generative adversarial network (GAN) to deal with the problem of imbalanced data classification. Firstly, we divide the majority class samples into three types according to the K-nearest neighbor algorithm. Secondly, undersampling is performed on the three types of data in the majority class through the clustering method. Then, a GAN model for tabular data generation is designed for oversampling of minority class samples. Finally, the preprocessed majority and minority data are used to train the machine learning model. We used real-world data sets to conduct relevant test experiments. The experimental results show that the imbalanced data processed by the method in this paper have achieved excellent results in the two evaluation indicators of the three common classification methods.

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Data availibility

The data used in this article is a common data set for imbalanced learning, which can be found here: http://www.keel.es/

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Acknowledgements

This research was supported by the National Key R &D Program of China (no. 2018YFC 1604000).

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Authors and Affiliations

  1. School of Cyber Science and Engineering, Wuhan University, Wuhan, China

    Hongwei Ding & Xiaohui Cui

  2. Key Laboratory of Aerospace Information Security and Trusted Computing, Wuhan University, Wuhan, China

    Hongwei Ding & Xiaohui Cui

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  1. Hongwei Ding
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  2. Xiaohui Cui
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Correspondence to Hongwei Ding.

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Ding, H., Cui, X. A clustering and generative adversarial networks-based hybrid approach for imbalanced data classification. J Ambient Intell Human Comput 14, 8003–8018 (2023). https://doi.org/10.1007/s12652-023-04610-z

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