首页|CMAGAN:classifier-aided minority augmentation generative adversarial networks for industrial imbalanced data and its application to fault prediction
CMAGAN:classifier-aided minority augmentation generative adversarial networks for industrial imbalanced data and its application to fault prediction
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维普
Class imbalance is a common characteristic of industrial data that adversely affects industrial data mining because it leads to the biased training of machine learning models.To address this issue,the augmentation of samples in minority classes based on generative adversarial networks(GANs)has been demonstrated as an effective approach.This study proposes a novel GAN-based minority class augmentation approach named classifier-aided minority augmentation generative adversarial network(CMAGAN).In the CMAGAN framework,an outlier elimination strat-egy is first applied to each class to minimize the negative impacts of outliers.Subsequently,a newly designed bound-ary-strengthening learning GAN(BSLGAN)is employed to generate additional samples for minority classes.By incorporating supplementary classifier and innovative training mechanism,the BSLGAN focuses on learning the distribution of samples near classification boundaries.Consequently,it can fully capture the characteristics of the target class and generate highly realistic samples with clear boundaries.Finally,the new samples are filtered based on the Mahalanobis distance to ensure that they are within the desired distribution.To evaluate the effectiveness of the proposed approach,CMAGAN was used to solve the class imbalance problem in eight real-world fault-prediction applications.The performance of CMAGAN was compared with that of seven other algorithms,including state-of-the-art GAN-based methods,and the results indicated that CMAGAN could provide higher-quality augmented results.
Class imbalanceMinority class augmentationGenerative adversarial network(GAN)Boundary strengthening learning(BSL)Fault prediction
Wen-Jie Wang、Zhao Liu、Ping Zhu
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School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,People's Republic of China
National Engineering Research Center of Automotive Power and Intelligent Control,Shanghai Jiao Tong University,Shanghai 200240,People's Republic of China
School of Design,Shanghai Jiao Tong University,Shanghai 200240,People's Republic of China
万方数据
维普
