# Hard Disk Failure Prediction Based on Blending Ensemble Learning
## 4 Experiment
### Looking for the Best Match  
In the above, we have proved that the blending ensemble learning method can improve the performance of the model. Next, we aim to find a combination of base learners that can achieve good performance on many hard disk models. Since each base learner has different detection strengths and accuracies for positive and negative samples, the ensemble learning method is comprised of the performance of each base learner, which can learn from each other and complement each other to achieve a more stable and efficient model. Taking the hard disk model ST8000 DM002 as an example, the number of false positives and false negatives in the results of each base learner is shown in Table 8.  
From Table 8, we can see that the random forest method has the best and very high anomaly detection accuracy, but the disadvantage is that the number of false-negative \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Blending Group A** & \begin{tabular}{c} **Blending** \\ **Group C** \\ \end{tabular} & \begin{tabular}{c} **Blending** \\ **Group B** \\ \end{tabular} & \begin{tabular}{c} **Blending** \\ **Group D** \\ \end{tabular} & \begin{tabular}{c} **Blending** \\ **Group D** \\ \end{tabular} & \begin{tabular}{c} **BIPNN** \\ \end{tabular} & \
\begin{tabular}{c} **LSTM** \\ \end{tabular} &
\begin{tabular}{c} **RF** \\ \end{tabular} \\ \hline ST8000 DM002 & 0.9644412 & 0.9679243 & 0.9627051 & **0.9709060** & 0.9167915 & 0.9698113 & 0.9400783 \\ ST8000 NM0055 & 0.9661928 & **0.9713085** & 0.9551134 & 0.9657260 & 0.9419506 & 0.9665092 & 0.9521076 \\ ST12000 NM0008 & 0.9398109 & **0.9440028** & 0.9161423 & 0.9256142 & 0.8900800 & 0.9330701 & 0.9191321 \\ HMS5C4040 BLE640 & 0.8307019 & **0.9761437** & 0.8081199 & 0.8075408 & 0.6542020 & 0.8029737 & 0.6737136 \\ MG07 ACA14 TA & **0.9838513** & 0.9829368 & 0.9763159 & 0.9792056 & 0.9733721 & 0.9828550 & 0.9805340 \\ MG08 ACA16 TE & **0.9211962** & 0.9185203 & **0.9211962** & 0.9190588 & 0.8851853 & 0.9159097 & 0.9105094 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Test results on different types of hard disk (The values in bold are the best results under the same conditions). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **RF** & **AdaBoost** & **GBDT** & **LSTM** & **LR** & **SVM** & **KNN** \\ \hline False Positive & 7 & 22 & 16 & 12 & 46 & 10 & 31 \\ False Negative & 66 & 25 & 23 & 25 & 29 & 23 & 38 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Individual test results for each model on the hard disk of model ST8000 DM02. samples is too high. Therefore, we take random forest as one of the base learners, and then select the three classification models with the lowest false negative rate, namely LSTM, GBDT, and SVM, as the other three base learners. We use these four models to form a new ensemble learning model to test the test set, and the number of false positives and false negatives are 9 and 23, respectively. The Matthews correlation coefficient is 0.9723992. Compared with the random Forest model alone, although the number of false positives is slightly increased, the number of false negatives is greatly reduced, and the comprehensive performance exceeds each individual model. The experiment results proved our idea, and the following experiment continued according to this idea. According to the above verification idea, experiments were carried out on the other five models of hard disks, and Table 9 was obtained. By observing the confusion matrices obtained from each method experimented on each hard disk model, we found that their performance bias is basically stable, while logistic regression and KNN basically always have a large number of false positives and false negatives, so we excluded these two classifiers when experimenting. We experimented with multiple ensemble combinations based on the results of the statistics, and the results are shown in Table 9. It can be seen that the combination of base learners in the last column achieved good results on the six hard disk datasets.