## 5 Evaluation and Findings
### Experimental Findings
#### 5.2.1 Running Time Analysis  
To assess the trade-off between enhanced performance and time-consuming cost in the proposed EHMFFL algorithm, we conducted a comprehensive analysis comparing its running time with the seven base learners and an ensemble model utilizing all the original features for each base learner, named \"Ensemble\" in Table 5. Our findings, detailed in Table 5, delineate the offline training/tuning phase and the online test phase time comparisons for both datasets. The offline time for each base learner signifies its individual training time, while for the Ensemble model, it represents the total training duration of the seven base learners within the model. Conversely, in our EHMFFL model, this duration encapsulates the time for feature selection via the PCC-GWO algorithm and training the base learners with the reduced feature subsets.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{2}{c}{**Offline Training/Tuning Phase**} & \multicolumn{2}{c}{**Online Test Phase**} \\ \cline{2-5}  & **Cleveland Dataset** & **Statlog Dataset** & **Cleveland Dataset** & **Statlog Dataset** \\ \hline LR & 0.01 & 0.01 & 0.02 & 0.02 \\ DT & 0.13 & 0.12 & 0.03 & 0.03 \\ RF & 0.74 & 0.7 & 0.08 & 0.07 \\ NB & 0.12 & 0.14 & 0.04 & 0.04 \\ SVM & 0.21 & 0.23 & 0.06 & 0.07 \\ KNNs & 0.08 & 0.07 & 0.03 & 0.02 \\ XGBoost & 0.86 & 0.83 & 0.1 & 0.09 \\ Ensemble & 2.3 & 2.2 & 0.17 & 0.16 \\ EHMFFL & 142 & 137 & 0.13 & 0.13 \\ (Proposed) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the running time (in seconds) of the different techniques.  
Figure 7: Comparison of the results of different methods using the Statlog dataset in terms of precision, recall, and accuracy.  
Analysis of Running Time in Offline Training/Tuning Phase: Table 5 indicates that the Ensemble model, leveraging all the original features for each base learner, approximately accumulates the total time consumption of the training phase for individual base learners, given their sequential training approach. However, the huge increase in the running time of the EHMFFL algorithm, compared to the Ensemble model, primarily stems from the implementation of the time-intensive PCC-GWO feature selection algorithm for selecting an appropriate feature subset for each base learner. Notably, these processes are confined to the offline model's tuning and do not affect the test phase time.  
Analysis of Running Time in Online Test Phase: In the online test phase, both the Ensemble and EHMFFL models exhibit slightly higher time consumption than the single base learners. As the test phase operations occur in parallel, the cumulative time of the base learners is not entirely additive for these models. While the Ensemble model shows around a 70% increase compared to the most time-consuming base learner (i.e., XGBoost), the EHMFFL model, owing to the PCC-GWO feature selection, presents reduced response times for the base learners compared to the Ensemble model. As a result, the online time of the EHMFFL algorithm exhibits a 24% and 19% reduction when using the Cleveland and Statlog datasets, respectively.  
Despite the significant increase in the offline running time of the EHMFFL model caused by the combined heuristic-metaheuristic PCC-GWO feature selection algorithm, the implementation of PCC-GWO yields a reduction in the online processing time when compared to the Ensemble model encompassing all the original features.