Data-level hybrid strategy selection for disk fault prediction model
  based on multivariate GAN
Shuangshuang Yuan Peng Wu Yuehui Chen
Data class imbalance is a common problem in classification problems, where
minority class samples are often more important and more costly to misclassify
in a classification task. Therefore, it is very important to solve the data
class imbalance classification problem. The SMART dataset exhibits an evident
class imbalance, comprising a substantial quantity of healthy samples and a
comparatively limited number of defective samples. This dataset serves as a
reliable indicator of the disc's health status. In this paper, we obtain the
best balanced disk SMART dataset for a specific classification model by mixing
and integrating the data synthesised by multivariate generative adversarial
networks (GAN) to balance the disk SMART dataset at the data level; and combine
it with genetic algorithms to obtain higher disk fault classification
prediction accuracy on a specific classification model.