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BCGAN: A CGAN-based over-sampling model using the boundary class for data balancing

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Abstract

A class imbalance problem occurs when a dataset is decomposed into one majority class and one minority class. This problem is critical in the machine learning domains because it induces bias in training machine learning models. One popular method to solve this problem is using a sampling technique to balance the class distribution by either under-sampling the majority class or over-sampling the minority class. So far, diverse over-sampling techniques have suffered from overfitting and noisy data generation problems. In this paper, we propose an over-sampling scheme based on the borderline class and conditional generative adversarial network (CGAN). More specifically, we define a borderline class based on the minority class data near the majority class. Then, we generate data for the borderline class using the CGAN for data balancing. To demonstrate the performance of the proposed scheme, we conducted various experiments on diverse imbalanced datasets. We report some of the results.

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Acknowledgements

This research was supported in part by Energy Cloud R&D Program (Grant Number: 2019M3F2A1073184) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT and in part by Government-wide R&D Fund project for infectious disease research (GFID), Republic of Korea (Grant Number: HG19C0682).

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Author notes

  1. Minjae Son and Seungwon Jung contributed equally to this work.

Authors and Affiliations

  1. Kyowon, Seoul, 04539, South Korea

    Minjae Son

  2. School of Electrical Engineering, Korea University, Seoul, 02841, South Korea

    Seungwon Jung, Seungmin Jung & Eenjun Hwang

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  1. Minjae Son
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Correspondence to Eenjun Hwang.

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Appendices

Appendix 1: Correlation matrices of datasets

For better understanding of the datasets used, we present the correlation matrices of each dataset. Figure 8 describes the obtained correlation matrices with heatmap.

Fig. 8
figure 8figure 8

Correlation matrices of all datasets: a Breast, b WDBC, c Wine, d Letter, e Surgery, f Yeast, g CMC, h Card, i Email, j Tel, k Bio, l Pay (color figure online)

Full size image

In the figure, each cell indicates the correlation between features in x-axis and y-axis by color. The closer the color is to dark blue, the higher the correlation value.

Appendix 2: Detailed results of Sect. 4.4

We present the more detailed results of the experiment in Sect. 4.4. Tables 5 and 6 show both averages and standard deviations of the obtained AUC. In each cell, the left value is the average AUC, and the right value is the standard deviation. We omit the standard deviations of the “Base” case because we did not conduct over-sampling in this case.

Table 5 Performance comparison of over-sampling methods and classification models with six datasets
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Table 6 Performance comparison of over-sampling methods and classification models with the others
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Son, M., Jung, S., Jung, S. et al. BCGAN: A CGAN-based over-sampling model using the boundary class for data balancing. J Supercomput 77, 10463–10487 (2021). https://doi.org/10.1007/s11227-021-03688-6

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