## 1 Introduction  
Nowadays, the scale of data centers has rapidly increased, because more data are generated everyday with the significant development of the internet and computer technologies. Among the limited kinds of storage devices in data centers, hard disk drives (HDDs) represent a large proportion, which is one of the main reasons behind server breakdowns in data centers, where around 82% of server breakdowns are caused by HDD failures [1], leading to data losses and economic losses. Consequently, improving HDD reliability is extremely important. Previously, researchers in the storage community have undertaken many works to make HDDs more reliable. Traditionally, some data redundancy methods such as replication in GFS [2], erasure code (EC) [3], and redundant arrays of independent disks (RAID) [4] have widely been deployed in modern storage systems as remedies in response to the occurrence of disk failure. However, these methods are reactive fault-tolerant techniques that reconstruct data when disk failures occur, thus waste storage space and bandwidth [5]. In recent years, machine learning technologies and artificial intelligence (ML/AI) have appealed to many researchers because of their significant success in data-driven applications. Thus, proactive failure prediction based on ML/AI is widely studied and has become a hot spot in the HDD reliability field. Compared to traditional reactive approaches, disk failure prediction can allow administrators to take precautions if failures are successfully predicted, resulting in lower costs for maintenance. Generally, self-monitoring, analysis, and reporting technology (SMART) [6] data are used for building prediction models, which are correlated with the disk health and indicative of eventual failure. Previously, many works have gained satisfactory results in HDD failure prediction [7; 8; 9]; for example, [PERSON] et al. [7] predicted disk failure with more than 95% accuracy based on the random forest (RF) model; and [PERSON] et al. [8] and [PERSON] et al. [9] obtained an extremely high prediction quality based on long short-term memory (LSTM). However, most of these works did not discuss timeliness. This is also crucial for disk failure prediction. On the one hand, the data of the disk state are updated in real-time. Thus, it is necessary to update prediction models to make them suitable for the latest forecast. In this case, building models quickly can improve the efficiency of models updating, which would make the latest predictions more accurate. On the other hand, the prediction latency may be too high to replace a failed disk in time, leading to data and economic losses; thus, reducing prediction latency is also critical. To speed up modeling, some works focused on updating hardware architectures, such as GPU and FPGA. Meanwhile, some techniques focused on model optimization such as Adam [10] and model compression methods such as network pruning. In this work, another perspective for model acceleration is considered; that is, reducing the input data features. This is proposed based on two observations: First, for neural networks such as convolutional neural networks (CNN), more training epochs always lead to lower training losses but higher latency. Second, using fewer attributes does not decrease a models' performance, but reduces the modeling latency. Thus, the purpose of this work was to improve the timeliness, without reducing the models' quality, by selecting as few attributes as possible. To realize this, a feature selection scheme was designed. The basic idea is to first classify attributes into different groups by mining the correlations between them and then to select a representative from each group for modeling. Thus, this would use fewer attributes to represent the overall data. However, there remains some challenges: First, there are attributes that may have negative impacts on modeling. In this case, some groups may be insensitive to predict failures and reduce the prediction quality.