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Data class imbalance is a common problem in classification problems, whereminority class samples are often more important and more costly to misclassifyin a classification task. Therefore, it is very important to solve the dataclass imbalance classification problem. The SMART dataset exhibits an evidentclass imbalance, comprising a substantial quantity of healthy samples and acomparatively limited number of defective samples. This dataset serves as areliable indicator of the disc's health status. In this paper, we obtain thebest balanced disk SMART dataset for a specific classification model by mixingand integrating the data synthesised by multivariate generative adversarialnetworks (GAN) to balance the disk SMART dataset at the data level; and combineit with genetic algorithms to obtain higher disk fault classificationprediction accuracy on a specific classification model.

Data-level hybrid strategy selection for disk fault prediction model based on multivariate GAN