Report
Data-level hybrid strategy selection for disk fault prediction model based on multivariate GAN
| العنوان: | Data-level hybrid strategy selection for disk fault prediction model based on multivariate GAN |
|---|---|
| المؤلفون: | Yuan, Shuangshuang, Wu, Peng, Chen, Yuehui |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2310.06537 |
| رقم الانضمام: | edsarx.2310.06537 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |