CMAGAN: classifier-aided minority augmentation generative adversarial networks for industrial imbalanced data and its application to fault prediction
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OpenAlex topics: Imbalanced Data Classification Techniques · Electricity Theft Detection Techniques · Anomaly Detection Techniques and Applications
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References (40)
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1. Jiang X, Ge Z (2021) Data augmentation classifier for imbalanced fault classification. IEEE Trans Autom Sci Eng 18(3):1206–121710.1109/TASE.2020.2998467 ↗
2. Liu F, Dai Y (2022) Product processing quality classification model for small-sample and imbalanced data environment. Comput Intell Neurosci 2022:9024165. https://doi.org/10.1155/2022/902416510.1155/2022/9024165 ↗
3. Li Z, Wang Y, Wang K (2017) Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers : Industry 4.0 scenario. Adv Manuf 5 (4):377‒38710.1007/s40436-017-0203-8 ↗
4. Zhuo Y, Ge Z (2020) Gaussian discriminative analysis aided GAN for imbalanced big data augmentation and fault classification. J Process Control 92:271–28710.1016/j.jprocont.2020.06.014 ↗
5. Lan Z, Huang G, Li Y et al (2022) Conquering insufficient/imbalanced data learning for the internet of medical things. Neural Comput Appl 35(31):22949–2295810.1007/s00521-022-06897-z ↗
6. Shao S, Wang P, Yan R (2019) Generative adversarial networks for data augmentation in machine fault diagnosis. Comput Ind 106:85–9310.1016/j.compind.2019.01.001 ↗
7. Islam A, Belhaouari SB, Rehman AU et al (2022) KNNOR: an oversampling technique for imbalanced datasets. Appl Soft Comput 115:108288. https://doi.org/10.1016/j.asoc.2021.10828810.1016/j.asoc.2021.108288 ↗
8. Krawczyk B, Wozniak M, Schaefer G (2014) Cost-sensitive decision tree ensembles for effective imbalanced classification. Appl Soft Comput 14:554–56210.1016/j.asoc.2013.08.014 ↗
9. Yang K, Yu Z, Wen X et al (2020) Hybrid classifier ensemble for imbalanced data. IEEE Trans Neural Netw Learn Syst 31(4):1387–140010.1109/TNNLS.2019.2920246 ↗
10. Madani M, Motameni H, Mohamadi H (2023) KNNGAN: an oversampling technique for textual imbalanced datasets. J Supercomput 79(5):5291–532610.1007/s11227-022-04851-3 ↗
11. Wei Z, Zhang L, Zhao L (2023) Minority-prediction-probability-based oversampling technique for imbalanced learning. Inf Sci 622:1273–129510.1016/j.ins.2022.11.148 ↗
12. Koziarski M (2021) Potential anchoring for imbalanced data classification. Pattern Recognit 120:108114. https://doi.org/10.1016/j.patcog.2021.10811410.1016/j.patcog.2021.108114 ↗
13. Xie Y, Qiu M, Zhang H et al (2022) Gaussian distribution based oversampling for imbalanced data classification. IEEE Trans Knowl Data Eng 34(2):667–67910.1109/TKDE.2020.2985965 ↗
14. Kaur H, Pannu HS, Malhi AK (2019) A systematic review on imbalanced data challenges in machine learning: applications and solutions. ACM Comput Surv 52(4):1–36
15. Liu X, Wu J, Zhou Z (2009) Exploratory undersampling for class-Imbalance learning. IEEE Trans Syst Man Cybern B 39(2):539–55010.1109/TSMCB.2008.2007853 ↗
16. Liu R (2023) A novel synthetic minority oversampling technique based on relative and absolute densities for imbalanced classification. Appl Intell 53(1):786–80310.1007/s10489-022-03512-5 ↗
17. Son M, Jung S, Jung S et al (2021) BCGAN: a CGAN-based over-sampling model using the boundary class for data balancing. J Supercomput 77(9):10463–1048710.1007/s11227-021-03688-6 ↗
18. Chawla NV, Bowyer KW, Hall LO et al (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–35710.1613/jair.953 ↗
19. He H, Bai Y, Garcia EA et al (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE international joint conference on neural networks, IEEE, pp 1322‒1328
20. Han H, Wang WY, Mao BH (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. Adv Intell Comput 3644:878–88710.1007/11538059_91 ↗
21. Douzas G, Bacao F, Last F (2018) Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Inf Sci 465:1–2010.1016/j.ins.2018.06.056 ↗
22. Goodfellow I, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27. https://doi.org/10.3156/jsoft.29.5_177_210.3156/jsoft.29.5_177_2 ↗
23. Qin Z, Liu Z, Zhu P et al (2022) Style transfer in conditional GANs for cross-modality synthesis of brain magnetic resonance images. Comput Biol Med 148:105928. https://doi.org/10.1016/j.compbiomed.2022.10592810.1016/j.compbiomed.2022.105928 ↗
24. Li Y, Gan Z, Shen Y et al (2019) StoryGAN: a sequential conditional GAN for story visualization. In: proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Long Beach, CA, USA, pp 6322‒633110.1109/CVPR.2019.00649 ↗
25. Yang G, Zhong Y, Yang L et al (2021) Fault diagnosis of harmonic drive with imbalanced data using generative adversarial network. IEEE Trans Instrum Meas 70:1–1110.1109/TIM.2021.3126366 ↗
26. Li J, Cao L, Liu H et al (2023) Imbalanced data generation and fusion for in-situ monitoring of laser powder bed fusion. Mech Syst Signal Process 199:110508. https://doi.org/10.1016/j.ymssp.2023.11050810.1016/j.ymssp.2023.110508 ↗
27. Li Y, Shi Z, Liu C et al (2022) Augmented time regularized generative adversarial network (ATR-GAN) for data augmentation in online process anomaly detection. IEEE Trans Autom Sci Eng 19(4):3338–335510.1109/TASE.2021.3118635 ↗
28. Yu Y, Guo L, Gao H et al (2022) PCWGAN-GP: a new method for imbalanced fault diagnosis of machines. IEEE Trans Instrum Meas 71:3180431. https://doi.org/10.1109/TIM.2022.318043110.1109/TIM.2022.3180431 ↗
29. Wang X, Jiang H, Liu Y et al (2023) Data-augmented patch variational autoencoding generative adversarial networks for rolling bearing fault diagnosis. Meas Sci Technol 34(5):055102. https://doi.org/10.1088/1361-6501/acb37710.1088/1361-6501/acb377 ↗
30. Wang X, Jiang H, Wu Z et al (2023) Adaptive variational autoencoding generative adversarial networks for rolling bearing fault diagnosis. Adv Eng Inform 56:102027. https://doi.org/10.1016/j.aei.2023.10202710.1016/j.aei.2023.102027 ↗
31. Odena A, Olah C, Shlens J (2017) Conditional image synthesis with auxiliary classifier GANs. In: International conference on machine learning, Sydney, Australia, 2017
32. Park N, Mohammadi M, Gorde K et al (2018) Data synthesis based on generative adversarial networks. arXiv:1806.03384, https://doi.org/10.14778/3231751.323175710.14778/3231751.3231757 ↗
33. Zhang Y, Zaidi N, Zhou J et al (2023) Interpretable tabular data generation. Knowl Inf Syst 65(7):2935–296310.1007/s10115-023-01834-5 ↗
34. Zhai J, Qi J, Zhang S (2022) Imbalanced data classification based on diverse sample generation and classifier fusion. Int J Mach Learn Cybern 13(3):735–75010.1007/s13042-021-01321-9 ↗
35. Mirza M, Osindero S (2014) Conditional generative adversarial nets. https://doi.org/10.48550/arXiv.1411.178410.48550/arXiv.1411.1784 ↗
36. Xu L, Skoularidou M, Cuesta-Infante A et al (2019) Modeling tabular data using conditional GAN. Adv Neural Inf Process Syst, 32. https://doi.org/10.48550/arxiv.1907.0050310.48550/arxiv.1907.00503 ↗
37. 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 472:326–33710.1016/j.neucom.2021.04.135 ↗
38. Choi E, Biswal S, Malin B et al (2017) Generating multi-label discrete patient records using generative adversarial networks.In: machine learning for healthcare conference, Northeastern University, 2017
39. Wen L, Zhang X, Li Q et al (2023) KGA: integrating KPCA and GAN for microbial data augmentation. Int J Mach Learn Cybern 14(4):1427–144410.1007/s13042-022-01707-3 ↗
40. De Maesschalck R, Jouan-Rimbaud D, Massart DL (2000) The mahalanobis distance. Chemometr Intell Lab Syst 50(1):1–1810.1016/S0169-7439(99)00047-7 ↗