## 3 Experiment Results
### Analysis of Results
#### 3.3.2 Comparison with SEMs  
The advantage of SEMs over IEMs is their ability to consider the interdependence of features. In this study, we compare CIFE, CMIM, JMIM, mRMR, and maxMIFS as representative SEMs with the proposed algorithm. Similar to the comparison with IEMs, we present the results of selecting between 5 and 40 features using these SEMs. Figure 4 shows the average classification accuracy of the SEMs using four ML algorithms on the BPEx dataset. As seen in Figure 4, for the four ML algorithms, the classification accuracy of all the SEMs shows a significant upward trend between 5 and 10 features, then becomes stable with minor fluctuations. Furthermore, all the SEMs exhibit comparable levels of classification accuracy, and it should be noted that the CIFE and CMIM methods slightly underperform compared to the other three methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Dataset**} & \multicolumn{6}{c}{**Execution Time (s)**} \\ \cline{2-7}  & **InfoGain** & **GainRatio** & **SU** & **ChiSquare** & **Fisher** & **MCMI** \\ \hline BPEx & 26.01 & 24.82 & 26.98 & 19.53 & **15.06** & 44.74 \\ BPSnp\_1 & 33.75 & 33.77 & 34.99 & 26.70 & **23.18** & 62.21 \\ BPSnp\_2 & 33.20 & 34.05 & 32.96 & 27.92 & **21.10** & 68.16 \\ BPSnp\_3 & 34.09 & 34.46 & 34.61 & 24.52 & **22.15** & 62.49 \\ BPPRRS & 23.35 & 21.87 & 23.58 & 18.73 & **13.87** & 38.47 \\ \hline \end{tabular}
\end{table}
Table 5: Execution time of the IEMs and MCMI.  
Figure 4: Classification accuracy of the SEMs on B16 Ex: **(a)** 10-fold cross-validation accuracy by using NB. **(b)** 10-fold cross-validation accuracy by using SVM. **(c)** 10-fold cross-validation accuracy by using C4.5. **(d)** 10-fold cross-validation accuracy by using RF.  
As with the comparison to IEMs, we chose the best classification accuracy and corresponding feature number of the SEMs using different ML algorithms, which then served as the benchmark results to be compared with those of the proposed algorithm. Table 6 presents the comparison results of the SEMs and the proposed algorithm. In the majority of cases, the proposed algorithm achieves the highest or near-highest classification accuracy. Additionally, the number of features obtained by the proposed algorithm is significantly smaller than that obtained by the SEMs in all instances. By further comparing the mean of classification accuracy and feature numbers shown in the last two rows of Table 6, it is evident that the proposed algorithm outperforms the SEMs in terms of classification accuracy and feature quality.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
Figure 5 shows the feature quality of the SEMs on the BPEx dataset. From Figure 5, it is evident that the classification accuracy of the SEMs significantly improves as the value of Rel increases. However, it's important to note that after reaching the peak value of Rel, the classification accuracy of the SEMs does not show notable changes and may slightly fluctuate with the addition of redundant features. This observation highlights that the classification performance and feature quality of the SEMs are highly dependent on the setting of the feature subset size.  
Table 7 shows the comparison of execution times between the SEMs and the proposed algorithm. Since the execution time of the SEMs varies with their preset feature number, we used the average number of features shown in Table 6 as the preset feature number for the experiment. From Table 7, it can be seen that the execution time of the proposed algorithm is less than that of the SEMs. In practical applications, the execution time of the SEMs far exceeds that of the proposed method, as they require iterative searches to find the optimal number of features.