# Hard Disk Failure Prediction Based on Blending Ensemble Learning
## 5 Conclusions and Prospect  
In this paper, we proposed a new failure prediction method based on the blending ensemble learning method, combining machine learning and deep learning networks to solve the problem of hard disk failure prediction, and proved its rationality and effectiveness through several groups of experiments. In the preliminary experiment, we constructed some groups of blending ensemble learning models according to the performance of the base learner and carried out more than ten groups of experiments using the public dataset collected by the BackBlaze Company. The experimental results were compared with the results of using traditional methods, including a BP neural network, random forest, and LSTM alone, with less training sets and training time, and better results were obtained. Next, we conducted experiments on generalization. 1. We use violin plots and heat maps to screen features and align features according to the correlation of each feature in the heat map, which greatly improves the generalization capability of the model. 2. In order to find a model that can achieve good results on all models of hard disk datasets, we tried various combinations of base learners after studying the confusion \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Best Result** & \multirow{2}{*}{**RF + SVM + GBDT + LSTM**} & \multirow{2}{*}{**RF + SVM + AdaBoost**} & \multirow{2}{*}{**RF + SVM + AdaBoost + LSTM**} \\  & **Before** & & & \\ \hline ST8000 DM002 & 0.9709060 & **0.9723992** & 0.9715661 & **0.9723992** \\ ST8000 NM0055 & 0.9713085 & **0.9760211** & 0.9742720 & **0.9760211** \\ ST12000 NM0008 & 0.9440028 & **0.9448954** & 0.9434789 & **0.9448954** \\ HMS5C4040 BLE640 & **0.9761437** & 0.8327314 & 0.9576024 & 0.9735438 \\ MG07 ACA14 TA & **0.9838513** & 0.9826042 & 0.9821888 & 0.9826045 \\ MG08 ACA16 TE & **0.9211962** & **0.9211962** & 0.9158109 & **0.9211962** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of experimental results of several basic learner combinations on various hard disk models (The values in bold are the best results under the same conditions). matrices obtained by each base learner on each dataset and finally found an ensemble model that has the best generalization capability. The next step of the current plan is to try to predict the remaining life of a hard disk, rather than simply classify the hard disk data as healthy or failed as a binary classification problem. In addition to this, trying to experiment with more advanced models is also the top priority of future work. Conceptualization, M.Z. and P.L. ; methodology, M.Z. ; software, W.G. ; resources, M.Z. ; data curation, M.Z. ; writing--original draft preparation, M.Z. ; writing--review and editing, M.Z. and P.L. ; supervision, P.L. and R.T.; project administration, M.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Publicly available datasets were analyzed in this study. This data can be found here: [[https://www.backblaze.com/](https://www.backblaze.com/)]([https://www.backblaze.com/](https://www.backblaze.com/)). We are grateful to the anonymous reviewers for comments on the original manuscript. The authors declare no conflict of interest. Main parameter values used in some machine learning methods. Those marked with '-' in the above two tables indicate that the algorithm does not have this parameter or uses the default parameter. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Predicting Hard Disk Failures in Data Centers Using Temporal Convolutional Neural Networks. In Proceedings of the Euro-Par 2020: Parallel Processing Workshops, Warsaw, Poland, 24-25 August 2020; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], Eds. ; Springer International Publishing: Cham, Switzerland, 2021; pp. 277-289. * [PERSON] et al.