## 5 Results and Analysis
### Results
#### 5.2.2 Prediction Quality  
Table 4 shows the prediction quality of all schemes under different ML/AI models. The metrics were precision, recall, and F1-score, as described above, and in this table, they are represented by Pre, Re, and F1, respectively. In each row, Group1 represents the results for the related scheme in the first evaluated group. Group2_Prs and Group2_Sprm are for the second group, where Group2_Prs represents the results after further reduction based on Pearson's correlation coefficient, and Group2_Sprm represents the results after further reduction based on Spearman's correlation coefficient. From the results, several conclusions can be drawn:  
First, compared with the other schemes in the first evaluated group, Entropy and Gini had the best performance. In all ML/AI models, the prediction quality of Entropy and Gini was similar to, or even better than, that of the _Original_. In addition, they obtained the best results when using RF and LSTM. This is one of the reasons why we design our attribute filtering module based on the idea of decision trees. Regarding the three other schemes, _Pearson_, _Spearman_, and _J-Index_, they presented a worse prediction quality for \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**DM000**} \\ \hline Original & Pearson & Spearman & Index & Entropy & Gini & Original & Pearson & Spearman & J-Index & Entropy & Gini \\
36 & 13 & 13 & 13 & 13 & 33 & 12 & 12 & 12 & 16 & 12 \\ \hline Org\_Prs & Prs\_Prs & Sprm\_Prs & Jidx\_Prs & Ent\_Prs & Gini\_Prs & Org\_Prs & Prs\_Prs & Sprm\_Prs & Jidx\_Prs & Ent\_Prs & Gini\_Prs \\
21 & 7 & 7 & 7 & 10 & 10 & 19 & 8 & 9 & 9 & 8 \\ \hline Org\_Sprm & Prs\_Sprm & Sprm\_Sprm & Sprm\_Sprm & Ent\_Sprm & Gini\_Sprm & Org\_Sprm & Prs\_Sprm & Sprm\_Sprm & Jidx\_Sprm & Ent\_Sprm & Gini\_Sprm \\
21 & 8 & 8 & 8 & 10 & 10 & 19 & 8 & 10 & 10 & 9 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of Attributes after Feature Selectionalmost all ML/AI models compared with the _Original_. In detail, _J-Index_ performed better than _Pearson_ and _Spearman_, where _Pearson_ and _Spearman_ had a similar prediction quality. On the one hand, this reflects the high effectiveness of the decision tree-based attribute selection methods and proves that a suitable feature selection method can improve the prediction quality. On the other hand, this implies that an unsuitable feature selection method will damage the prediction quality of ML/AI models. Second, in most cases, the prediction quality after deep attribute reduction was similar to before. This can be observed in the NB, RF, GBDT, CNN, and LSTM for all schemes. In some cases, the prediction quality was even improved after attribute reduction. For example, in DM000, all metrics of _Org_Sprm_ using LSTM were better than the _Original_; in NM0007, all metrics of _Org_Prs_ using LSTM were better than the _Original_. In most situations, it seems that there are no obvious advantages to using [PERSON]'s or Spearman's correlation coefficient, because in some models, the prediction quality of _Prs_ is better, and in some cases, the other performs better. For example, for SVM, _Org_Sprm_ performed much better than _Org_Prs_ in DM000; and in NM0007, _Org_Prs_ performed better than _Org_Sprm_. However, _Gini_Prs_ obtained the best prediction quality among all schemes when using  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{10}{c}{**DM000**} & \multicolumn{10}{c}{**NM0007**} \\ \cline{3-14}  & & \multicolumn{4}{c}{**Group1**} & \multicolumn{4}{c}{**Group2\_Prs**} & \multicolumn{4}{c}{**Group2\_Sprm**} & \multicolumn{4}{c}{**Group1**} & \multicolumn{4}{c}{**Group2\_Prs**} & \multicolumn{4}{c}{**Group2\_Sprm**} \\ \cline{3-14}  & &  
RF in DM000 (precision, recall, and F1-score were up to 1, 0.99, and 0.99, respectively), and it achieved the best prediction quality when using LSTM in NM0007 (precision, recall, and F1-score were up to 1, 0.99, and 0.99, respectively).