## 4 Results and Discussion
The performances of the SVM and NB classifiers are also illustrated in Figure 4, which compares the receiver operating characteristic (ROC) curves for these classifiers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No.** & **SMART ID** & **Attribute Name** \\ \hline
1 & 4 & Start/Stop Count \\
2 & 7 & Seek Error Rate \\
3 & 12 & Power Cycle Count \\
4 & 189 & High Fly Writes \\
5 & 193 & Load/Unload Cycle Count \\
6 & 194 & Temperature \\
7 & 197 & Current Pending Sector Count \\
8 & 198 & Uncorrectable Sector Count \\
9 & 199 & UltraDMA CRC Error Count \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Features selected by the proposed two-tier feature selection process. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Feature Vector** & **No. of Folds** & **No. of Test** & **False Positive** & **True Positive** & **Average** \\  & **Dimensionality** & **for Cross Validation** & **Iterations** & **Rate (\%)** & **Rate (\%)** & **Accuracy (\%)** \\ \hline Naive Bayes with No Feature Selection & 42 & 3 & 10 & 1.03 & 55.2 & 86.98 \\ Naive Bayes with GA only & 12 & 3 & 10 & 0.92 & 72.0 & 92.0 \\ Naive Bayes with Proposed Two-Tier Method & 9 & 3 & 10 & 0.24 & 98.4 & 99.01 \\ SVM with No Feature Selection & 42 & 3 & 10 & 74.95 & 75.0 & 20.56 \\ SVM with GA only & 12 & 3 & 10 & 0.26 & 40.0 & 83.3 \\ SVM with Proposed Two-Tier Method & 9 & 3 & 10 & 0.6 & 44.15 & 84.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A comparison of the performance achieved by the Naive Bayes and Support Vector Machine classifiers using different feature selection methods. The ROC curves in Figure 4 clearly indicate that the NB classifier trained on the nine features selected by the proposed two-tier feature selection process yields the best results in terms of both the TPR and FPR. When compared to existing methods for detecting failing hard disks, the proposed method offers distinct advantages. [PERSON] et al. [18] presented a two-step parametric method, which utilizes 47 critical features, identified in [17] for failure prediction in HDD, as opposed to the nine SMART attributes determined in this study. They tested their method on a dataset that was collected for 369 hard disks of a single model, and contained data only for the last 600 h, where each sample of data are 2 h apart from the next one. Thus, for each drive, a maximum of 300 values are available for each of the 47 features. Among the 369 drives, 178 drives are healthy, while 191 are failed drives. Given the variation in failure rates across different models and across different storage capacities for certain models, as observed in commercial datacenters [41], this dataset cannot be considered a representative dataset. In contrast, as discussed in Section 2, the proposed method is tested on a more extensive and more representative dataset. The method proposed in [18] could achieve a failure detection rate (FDR) or TPR of 68.42% at a FAR of 0%, and an FDR of around 95% at a FAR of around 4.2%. However, the FAR is highly sensitive to the failure threshold and no mechanism has been provided to set an appropriate failure threshold. This is an important concern because the ineffectiveness of the simple thresholding technique put in place by drive manufacturers to detect a failing hard disk was a major reason why considerable interest was generated among researchers to devise better methods for detecting failing disk drives. The proposed method uses a two-tier feature selection process, and determines the nine SMART attributes given in Table 2, to be the most effective precursors to a failing HDD. This reduces the training time of the classifier. Using the values of the nine selected SMART attributes for 2065 hard disks, the proposed method yields an FDR of 98.40% at a FAR of 0.24%, without using any arbitrary parameters. Among the 2065 hard drives, 1500 are healthy and 565 are failed drives. These are divided into three folds, with 688, 688, and 689 hard drives, respectively.