Hard drive failure prediction using non-parametric statistical methods
نویسندگان
چکیده
We present a case study of a difficult real-world pattern recognition problem: predicting hard drive failure using attributes monitored internally by individual drives. We compare the performance of support vector machines (SVMs), unsupervised clustering, and non-parametric statistical tests (rank-sum and reverse arrangements). Somewhat surprisingly, the rank-sum method outperformed the other methods, including SVMs. We also show the utility of using non-parametric tests for feature set selection. Keywords— failure prediction, hard drive reliability, ranksum, reverse arrangements, support vector machines,
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