## 5 Results and Analysis
### Results
#### 5.2.3 Modeling Latency  
In this section, the modeling latency is divided into training and prediction latency. Figure 6 shows the normalized training latency for all schemes under all ML/AI models in both DM000 and NM0007. The analysis of the results is as follows:  
Figure 6: Normalized training latency for the two models: (**a**) the latency for DM000; (**b**) the latency for NM0007. Original showed the highest latency in all situations. For the first evaluated group, for most models, including NB, SVM, and CNN, the training latency decreased as the number of attributes was reduced. For example, for CNN in Figure 6a, _Pearson_, _Spearman_, _J-Index_, _Entropy_, and _Gini_ reduced the latency by 66%, 67%, 66%, 67%, and 67%, respectively, compared with the _Original_. When comparing them with the second evaluated group, the latency of all schemes decreased after attribute reduction in almost all ML/AI models (except LSTM). For example, in DM000, _Org_Prs_ and _Org_Sprm_ reduced the latency by 20-50% compared with the _Original_; _Prs_Prs_ and _Prs_Sprm_ reduced the latency by 25-49% compared with _Pearson_; _Sprm_Prs_ and _Sprm_Sprm_ reduced the latency by 10-45% compared with _Spearman_; _Jidx_Prs_ and _Jidx_Sprm_ reduced the latency by 2-28% compared with _J-Index_; _Ent_Prs_ and _Ent_Sprm_ reduced the latency by 1-39% compared with _Entropy_; and _Gini_Prs_ and _Gini_Sprm_ reduced the latency by 13-31% compared with _Gini_. For NM0007, the trend was similar to that for DM000. In this case, although in some ML/AI models, the comparison results for the first group did not have a clear trend with the decrease in the number of attributes, the latency reduced as the number of attributes was reduced based on our attributes reduction method. On the one hand, this implies that the number of attributes indeed correlates with the modeling latency. On the other hand, this can prove the high effectiveness of our attribute reduction method. Overall, our proposed scheme _Gini_Prs_ could reduce the training latency by up to 75% compared with the _Original_, with a relatively high prediction quality. For the tree-based ML/AI models including RF and GBDT, _Pearson_ and _Spearman_ always showed the lowest latency, and _J-Index_ presented the second-lowest latency. The reason for this is that, for tree-based models, the modeling latency is closely related to the complexity of tree structures. If the Gini index and entropy reduction based on certain attributes are too low, then they are not immediately selected as split nodes, thus reducing the calculations and the tree structure would be simpler. However, compared with other schemes, _Pearson_, _Spearman_, and _J-Index_ showed a lower prediction quality, due to the lack of some critical attributes. Regarding LSTM, compared with the other ML/AI models, reducing attributes resulted in a relatively small reduction in training latency. In the first evaluated group, _Pearson_, _Spearman_, _J-Index_, _Entropy_, and _Gini_ reduced the training latency by 1-12% compared with the _Original_. When comparing the second group, they showed different trends in DM000 and NM0007. In DM000, _Org_Prs_ and _Org_Sprm_ reduced the training latency by 25% and 17% compared with _Original_; _Prs_Prs_ and _Prs_Sprm_ reduced the training latency by around 5% compared with _Original_; _Ent_Prs_ and _Ent_Sprm_ reduced the training latency by around 7% compared with _Entropy_. In NM0007, _Org_Prs_ and _Org_Sprm_ reduced the training latency by 13% and 8% compared with _Original_; _Jidx_Prs_ and _Jidx_Sprm_ reduced the training latency by around 9% compared with _J-Index_; and _Gini_Prs_ reduced the latency by 3% compared with _Gini_. For other schemes, the decrease ratio was less than 2% and the latency even improved a little. The reasons behind this may relate to the LSTM structure and the system architecture used. In this case, the number of attributes was too low for LSTM modeling and this may not be the main bottleneck.