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An intra-class distribution-focused generative adversarial network approach for imbalanced tabular data learning

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Abstract

Data imbalance is a critical factor that adversely affects the performance of machine learning algorithms. It leads to deviations in decision boundaries, resulting in biased predictions towards the majority class and inaccurate classification of the minority class. Although oversampling the minority class using deep generative models is a popular strategy, many existing methods focus solely on enhancing data for the minority class while overlooking the distribution relationship within and between classes. Therefore, we propose an oversampling method that merges unsupervised clustering and generative adversarial network (GAN) to facilitate the imbalanced tabular data learning. First, we perform preprocessing (clustering) on the original data, remove clusters that do not require sampling and generate more samples for sparsely distributed minority class clusters to achieve sample balance within the minority class. Moreover, we design a CTGAN-based auxiliary classifier GAN (ACCTGAN) to generate the minority class. It enhances the semantic integrity of the synthetic data and avoids generating noisy samples. We conducted validation experiments comparing our approach to 7 typical methods on 12 real tabular datasets. Our method shows excellent performance in F1-measure and area under the curve (AUC), obtaining 19 and 20 best results on the three classifiers, respectively. It significantly enhances classification results and demonstrates good robustness and stability.

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Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Hassan SM, Ali SA, Hassan B et al (2022) Hybrid Features Binary Classification of Imbalance Stroke Patients Using Different Machine Learning Algorithms. Int J Bio Biomed Eng 16:154–160

    Article  Google Scholar 

  2. Sapre S, Islam K, Ahmadi P (2021) A comprehensive data sampling analysis applied to the classification of rare iot network intrusion types. IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) 2021:1–2

    Google Scholar 

  3. Jedrzejowicz J, Jedrzejowicz P (2021) GEP-based classifier for mining imbalanced data. Expert Syst Appl 164:114058

    Article  Google Scholar 

  4. Fernandez A, Garcia S, Herrera F, Chawla NV (2018) SMOTE for learning from imbalanced data:progress and challenges, marking the 15-year anniversary. J Artificial Intellig Res 61:863–905

    Article  MathSciNet  Google Scholar 

  5. Zhang L, Zhang D (2016) Evolutionary cost-sensitive extreme learning machine. IEEE Trans Neural Netw Learn Syst 28(12):3045–3060

    Article  MathSciNet  Google Scholar 

  6. Shi L, Ma X, Xi L, Duan Q, Zhao J (2011) Rough set and ensemble learning based semi-supervised algorithm for text classification. Expert Syst Appl 38(5):6300–6306

    Article  Google Scholar 

  7. Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F (2011) A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42(4):463–484

    Article  Google Scholar 

  8. Batista GE, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter 6(1):20–29

    Article  Google Scholar 

  9. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inform Process Syst 2014:2672–2680

    Google Scholar 

  10. Chen MY, Chiang HS, Huang WK (2022) Efficient Generative Adversarial Networks for Imbalanced Traffic Collision Datasets. IEEE Trans Intel Trans Syst 23(10):19864–19873

    Article  Google Scholar 

  11. Dong Y, Xiao H, Dong Y (2022) SA-CGAN: An oversampling method based on single attribute guided conditional GAN for multi-class imbalanced learning. Neurocomputing 472:326–337

    Article  Google Scholar 

  12. Fan M, Yang Q, Zhang B, Zhang K, Xia J (2021) Cluster-based Generative Adversarial Network Imbalanced Data Generation Method. IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) 2021:547–552

    Google Scholar 

  13. Chawla NV, Japkowicz N, Drive P (2004) Editorial: special issue on learning from imbalanced data sets. ACM Sigkdd Exp 6(1):1–6

    Article  Google Scholar 

  14. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artificial Intellig Res 16:321–357

    Article  Google Scholar 

  15. Zhu Y, Yan Y, Zhang Y, Zhang Y (2020) EHSO: Evolutionary Hybrid Sampling in overlapping scenarios for imbalanced learning. Neurocomputing 417:333–346

    Article  Google Scholar 

  16. Han H, Wang WY, Mao BH (2005) Borderline-SMOTE: a new oversampling method in imbalanced data sets learning. Advances in Intelligent Computing: International Conference on Intelligent Computing 2005:878–887

    Article  Google Scholar 

  17. He H, Bai Y, Garcia EA, Li S (2008) ADASYN: Adaptive synthetic sampling approach for imbalanced learning. IEEE international joint conference on neural networks (IEEE world congress on computational intelligence) 2008:1322–1328

    Google Scholar 

  18. Douzas G, Bacao F, Last F (2018) Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Inform Sci 465:1–20

    Article  Google Scholar 

  19. Maldonado S, Vairetti C, Fernandez A, Herrera F (2022) FW-SMOTE: A feature-weighted oversampling approach for imbalanced classification. Pattern Recognition 124:108511

    Article  Google Scholar 

  20. Bej S, Davtyan N, Wolfien M, Nassar M, Wolkenhauer O (2021) Loras: an oversampling approach for imbalanced datasets. Machine Learn 110:279–301

    Article  MathSciNet  Google Scholar 

  21. Wang X, Xu J, Zeng T, Jing L (2021) Local distribution-based adaptive minority oversampling for imbalanced data classification. Neurocomput 422:200–213

    Article  Google Scholar 

  22. Xie X, Liu H, Zeng S, Lin L, Li W (2021) A novel progressively undersampling method based on the density peaks sequence for imbalanced data. Knowledge-Based Syst 213:106689

    Article  Google Scholar 

  23. Dai Q, Liu J, Shi Y (2023) Class-overlap undersampling based on Schur decomposition for Class-imbalance problems. Expert Syst Appl 221:119735

    Article  Google Scholar 

  24. Ng WWY, Xu S, Zhang J, Tian X, Rong T, Kwong S (2020) Hashing-Based Undersampling Ensemble for Imbalanced Pattern Classification Problems. IEEE Trans Cyber 52(2):1269–1279

    Article  Google Scholar 

  25. Mirzaei B, Nikpour B, Nezamabadi-pour H (2021) Cdbh: a clustering and density-based hybrid approach for imbalanced data classification. Expert Syst Appl 164:114035

    Article  Google Scholar 

  26. Khan SH, Hayat M, Bennamoun M, Sohel F, Togneri R (2017) Cost sensitive learning of deep feature representations from imbalanced data. IEEE Trans Neural Network Learn Syst 29(8):3573–3587

    Google Scholar 

  27. Fu S, Yu X, Tian Y (2022) Cost sensitive v-support vector machine with LINEX loss. Inform Processing Manag 59(2):102809

    Article  Google Scholar 

  28. Zhang S (2020) Cost-sensitive knn classification. Neurocomputing 391:234–242

    Article  Google Scholar 

  29. Zhang H, Jiang L, Li C (2021) CS-ResNet: Cost-sensitive residual convolutional neural network for PCB cosmetic defect detection. Expert Syst Appl 185(1):115673. https://doi.org/10.1016/j.eswa.2021.115673

    Article  Google Scholar 

  30. Chen Z, Duan J, Kang L, Qiu G (2021) Class-imbalanced deep learning via a class-balanced ensemble. IEEE Trans Neural Netw Learn Syst 33(10):5626–5640

    Article  Google Scholar 

  31. Yang K, Yu Z, Wen X, Cao W, Chen CP, Wong HS, You J (2019) Hybrid classifier ensemble for imbalanced data. IEEE Trans Neural Netw Learn syst 31(4):1387–1400

    Article  MathSciNet  Google Scholar 

  32. Miyato T, Kataoka T, Koyama M, Yoshida Y (2018) Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802-05957

  33. Douzas G, Bacao F (2018) Effective data generation for imbalanced learning using conditional generative adversarial networks. Expert Syst Appl 91:464–471

    Article  Google Scholar 

  34. Odena A, Olah C, Shlens J (2017) Conditional image synthesis with auxiliary classifier gans. Int Conference Machine Learn PMLR 2017:2642–2651

    Google Scholar 

  35. Zheng M, Li T, Zhu R, Tang Y, Tang M, Lin L, Ma Z (2020) Conditional Wasserstein generative adversarial network-gradient penalty-based approach to alleviating imbalanced data classification. Inf Sci 512:1009–1023

    Article  Google Scholar 

  36. Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of wasserstein gans. Adv Neural Inform Proces Sys 2017:30

    Google Scholar 

  37. Engelmann J, Lessmann S (2021) Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning. Expert Syst with Appl 174:114582

    Article  Google Scholar 

  38. Xu L, Skoularidou M, Cuesta-Infante A, Veeramachaneni K (2019) Modeling tabular data using conditional gan. Adv Neural Inform Proces Syst 2019:32

    Google Scholar 

  39. Zhang Y, Liu Y, Wang Yan, Yang Jie (2023) An ensemble oversampling method for imbalanced classification with prior knowledge via generative adversarial network. Chemomet Intellig Labor Syst 2023(235):104775

    Article  Google Scholar 

  40. An C, Sun J, Wang Y, Wei Q (2021) A K-means Improved CTGAN Oversampling Method for Data Imbalance Problem. IEEE 21st International Conference on Software Quality, Reliability and Security (QRS) 2021:883–887

    Article  Google Scholar 

  41. Jo W, Kim D (2022) OBGAN: Minority oversampling near borderline with generative adversarial networks. Expert Syst Appl 197:116694

    Article  Google Scholar 

  42. Ding H, Sun Y, Huang N, Shen Z, Wang Z, Iftekhar A, Cui X (2023) RVGAN-TL: A generative adversarial networks and transfer learning-based hybrid approach for imbalanced data classification. Inform Sci 629:184–203

    Article  Google Scholar 

  43. Chinrungrueng C, Sequin CH (1995) Optimal adaptive k-means algorithm with dynamic adjustment of learning rate. IEEE Trans Neural Netw 6(1):157–169

    Article  Google Scholar 

  44. Lin Z, Khetan A, Fanti G, Oh S (2018) Pacgan: The power of two samples in generative adversarial networks. Adv Neural Inform Proces Syst 2018:31

    Google Scholar 

  45. Sch olkopf, B., Smola A, M uller K, (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299–1319

  46. Kwedlo W (2011) A clustering method combining differential evolution with the K-means algorithm. Pattern Recog Lett 32(12):1613–1621

    Article  Google Scholar 

  47. Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported partially by the National Natural Science Foundation of China [61972096, 61771140, 61872088, 61872090, 61902289], and the University-Industry Cooperation of Fujian Province [2022H6025].

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Authors and Affiliations

  1. College of Computer and Cyber Security, Fujian Normal University, Fuzhou, 350007, China

    Qiuling Chen, Ayong Ye, Yuexin Zhang, Jianwei Chen & Chuan Huang

  2. Fujian Provincial Key Laboratory of Network Security and Cryptology, Fuzhou, 350007, China

    Qiuling Chen, Ayong Ye, Yuexin Zhang, Jianwei Chen & Chuan Huang

Authors
  1. Qiuling Chen
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  2. Ayong Ye
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  3. Yuexin Zhang
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  4. Jianwei Chen
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Contributions

Qiuling Chen: Methodology, Formal analysis, and Writing-Original Draft; Ayong Ye: Conceptualization, Resources, Writing-Review & Editing and Funding acquisition; Yuexin Zhang: Writing-Reviewing and Editing; Jianwei Chen: Writing-Reviewing and Editing; Chuan Huang: Supervision.

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Correspondence to Ayong Ye.

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Chen, Q., Ye, A., Zhang, Y. et al. An intra-class distribution-focused generative adversarial network approach for imbalanced tabular data learning. Int. J. Mach. Learn. & Cyber. (2024). https://doi.org/10.1007/s13042-023-02048-5

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