## 4 Results and Discussion  
This section presents the results of this study, and provides a discussion in the context of existing research. After the necessary preprocessing, as discussed in Section 3, the SMART data are used by the GA to select the optimal features that will minimize the proposed fitness function. The subset of features selected by the GA is given in Table 1. The GA reduces the dimensionality of the original feature space from 42 to 12. It utilizes a population of 100 chromosomes over a maximum of 80 generations to optimize the fitness function discussed in Section 2. The GA selects a subset of features, which might work well as a combination but might contain sub-optimal features. Those sub-optimal features might not be significant contributors in determining whether an HDD is failing or not. This is why, in the proposed two-tier feature selection process, all the features selected by the GA are subsequently evaluated by calculating their feature significance scores, as discussed in Section 2.3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No.** & **SMART ID** & **Attribute Name** \\ \hline
1 & 3 & Spin-up Time \\
2 & 4 & Start/Stop Count \\
3 & 7 & Seek Error Rate \\
4 & 10 & Spin Retry Count \\
5 & 12 & Power Cycle Count \\
6 & 187 & Reported Uncorrected Errors \\
7 & 189 & High Fly Writes \\
8 & 193 & Load/Unload Cycle Count \\
9 & 194 & Temperature \\
10 & 197 & Current Pending Sector Count \\
11 & 198 & Uncorrectable Sector Count \\
12 & 199 & UltraDMA CRC Error Count \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Features selected by the Genetic Algorithm only. The second tier of the proposed feature selection process discards the features at serial numbers 1, 4, and 6, which are the SMART attributes recording the _spin-up time_, _spin retry count_, and _reported uncorrectable errors_, respectively. The final list of the nine features selected by the proposed two-tier feature selection process is given in Table 2. The effectiveness of the proposed two-tier feature selection process is demonstrated by the results in Table 3. When an NB classifier is trained using the features selected by the proposed two-tier approach, given in Table 2, it achieves an average classification accuracy of 99.01% with a false positive rate (FPR) of 0.24%. However, when the NB classifier is trained using different sets of SMART attributes, the results are not as promising. For example, when all 42 attributes are used to train the classifier, the average classification accuracy drops to 86.98%, while the FPR rises to 1.03%. The features selected by the GA alone are effective, but contain certain SMART attributes, which are not significant contributors in determining a failing disk drive, as evident from the average accuracy of 92.0% and an FAR of 0.92%. Table 3 also provides a comparison of NB with a discriminative classifier, such as an SVM. The SVM achieves an average accuracy of 83.30% and an FAR of 0.26%, when it is trained on the features selected by the GA alone. It achieves a marginal improvement in average accuracy when it is trained on the nine features selected by the proposed two-tier approach. However, it shows degradation in both the TPR and FPR, i.e., the TPR drops to 44%, whereas the FPR exhibits a small increase of 0.6%. The results in Table 3 indicate that the NB classifier performs better than SVM in predicting failing disk drives. Moreover, the results obtained using two different types of classifiers indicate that the SMART attributes selected by the proposed two-tier feature selection method yield better diagnostic performance in detecting failing and healthy HDD. As discussed earlier, the SVM, being a discriminative classifier, models the direct relationship between the feature vectors and the class labels. This approach may not be effective in the case of SMART data, where a direct relation between the two is not always very conclusive.