Name
Title | ||
|---|---|---|
Evaluating Feature Selection and Anomaly Detection Methods of Hard Drive Failure Prediction |
Abstract | ||
|---|---|---|
As vast amounts of data are saved, hard drive failure prediction is critical to reducing the cost of data loss and backup. Most existing studies used to detect the anomalous status of a hard drive using self-monitoring, analysis, and reporting technology (SMART) attributes, and then predicted whether the drive would have an impending failure. However, as most researchers focus on a specific model ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TR.2020.2995724 | IEEE Transactions on Reliability |
Keywords | DocType | Volume |
Feature extraction,Anomaly detection,Prediction algorithms,Robustness,Decision trees,Predictive models,Support vector machines | Journal | 70 |
Issue | ISSN | Citations |
2 | 0018-9529 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qibo Yang | 1 | 7 | 1.83 |
| Xiaodong Jia | 2 | 10 | 2.59 |
| Xiang Li | 3 | 56 | 6.55 |
| Jianshe Feng | 4 | 1 | 1.37 |
| wenzhe li | 5 | 67 | 5.53 |
| Jay Lee | 6 | 46 | 6.14 |