## 1 Introduction
It monitors different operational parameters and features of a disk drive, such as _temperature, read-write errors_, and _power-on hours_, which can be used by the basic input output system (BIOS) or client software to detect an imminent failure. The simplest way to use these attributes to detect a failing disk has been to determine whether a particular SMART attribute has exceeded a predetermined threshold or not [11; 13]. However, among the various SMART attributes, identifying the ones that are more indicative of a failing disk drive is not easy. There is no broad consensus on the precursors of hard disk failure among researchers. For example, the study in [16] concludes that there is no significant correlation between disk failure and its temperature and activity level; this assertion, however, is disputed by later studies [7; 22]. In [7], the authors find a direct relationship between increasing AFRs and increasing disk temperatures, whereas, in [22], hard disk failures are found to be inversely related to the temperature, i.e., hotter disks are less likely to fail than cooler disks. The authors assume that this may be due to higher relative humidity experienced by the cooler disks. It's important to note that both [7; 22] studies are based on the IT infrastructures of the same company. In [16], the authors acknowledge a high correlation between several SMART attributes and disk failures; they, however, rule out their utility at predicting the failure of individual disk drives. Nevertheless, several studies have demonstrated that SMART features can, in fact, be used to predict impending failure in disk drives [9; 12; 13; 15; 17; 18; 19; 20; 21; 23]. Many of these studies [9; 12; 19; 21; 23; 24] do not provide a sound mechanism for selecting the SMART attributes that can serve as the most effective precursors for an impending failure. The selection of good SMART attributes is very important for improving the detection accuracy and reducing the false alarm rate (FAR). Moreover, because of the inherent class imbalance in the available data, i.e., an average ARR of 4% means that 96% of drives are healthy and only 4% fail, reducing the FAR is even more significant, e.g., for an infrastructure with \(100,000\) disk drives, a reduction of 0.01% in the FAR would result in 1000 fewer replacements of otherwise healthy disk drives. Thus, even a small improvement in the FAR would save the time, effort, and bandwidth required to perform unwarranted disk replacements and the associated data transfers. In this paper, a two-tier approach is proposed to select the most indicative SMART attributes as precursors to a failing hard disk. In the first tier, a genetic algorithm (GA) is used for feature (or attribute) subset selection, whereas, in the second tier, a feature significance function is used to examine the selected subset of features and find those SMART attributes that would result in the best detection accuracy while minimizing the FAR. The GA selects the optimal SMART attributes using a mechanism that is analogous to biological evolution, where the principles of natural selection prune the sub-optimal characteristics in living organisms, over successive generations. In this study, the GA uses the ratio of the compactness of a class to the separability between classes as the objective function, and finds the optimal subset of SMART attributes that minimizes the objective function over successive generations. While the GA evaluates the SMART attributes in different combinations, the feature significance function considers individual attributes and discards those that are not significantly related to drive failures. A naive Bayes (NB) classifier is then used to construct a model for the selected SMART attributes, which can differentiate healthy drives from failure-prone drives. Moreover, because of the inherent imbalance between the number of failed and healthy drives, random under-sampling of the majority class is used to mitigate the effects of class imbalance on the performance of the classifier.