An improved CTGAN for data processing method of imbalanced disk failure
Jingbo Jia Peng Wu Hussain Dawood
To address the problem of insufficient failure data generated by disks and
the imbalance between the number of normal and failure data. The existing
Conditional Tabular Generative Adversarial Networks (CTGAN) deep learning
methods have been proven to be effective in solving imbalance disk failure
data. But CTGAN cannot learn the internal information of disk failure data very
well. In this paper, a fault diagnosis method based on improved CTGAN, a
classifier for specific category discrimination is added and a discriminator
generate adversarial network based on residual network is proposed. We named it
Residual Conditional Tabular Generative Adversarial Networks (RCTGAN). Firstly,
to enhance the stability of system a residual network is utilized. RCTGAN uses
a small amount of real failure data to synthesize fake fault data; Then, the
synthesized data is mixed with the real data to balance the amount of normal
and failure data; Finally, four classifier (multilayer perceptron, support
vector machine, decision tree, random forest) models are trained using the
balanced data set, and the performance of the models is evaluated using G-mean.
The experimental results show that the data synthesized by the RCTGAN can
further improve the fault diagnosis accuracy of the classifier.