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

• URL: http://arxiv.org/abs/2310.06537v1
• Date: Tue, 10 Oct 2023 11:34:53 GMT
• Title: Data-level hybrid strategy selection for disk fault prediction model based on multivariate GAN
• Authors: Shuangshuang Yuan, Peng Wu and Yuehui Chen
• Abstract summary: Data class imbalance is a common problem in classification problems, where minority class samples are often more important and more costly to misclassify. 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.
• Score: 7.270429986841776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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