## 5 Experimental Results
### The Dynamic Nature of DFSS  
We assessed the effectiveness of the DFSS in capturing temporal changes. This entailed identifying the optimal feature selection algorithms, their respective sets, and the contributions and percentages of feature classes for each sector over time. Table 5 displays the dynamic adjustments in the optimal feature sets for each sector across various time periods. The table demonstrates that the algorithms yielding optimal performance, along with their corresponding feature sets, differ for each sector over the timeline. This observation underscores the DFSS's capability to dynamically adjust the optimal feature sets at specific time points.  
Figures 4 and 5 depict the evolution of feature class inclusion percentages and feature importance rankings within the DFSS over time. Specifically, Figure 4 illustrates that, with the exception of the consumer staples and healthcare sectors, the trends in the inclusion rates of feature classes in other sectors vary over time. This suggests that the consumer staples and healthcare sectors, being essential consumption markets, exhibit less significant variability in the trends of feature inclusion. Additionally, Figure 5 reveals that there is minimal or no variation in the trends of feature importance across almost all sectors over time. This implies that the DFSS effectively mirrors the impact of certain well-established factors in the real world.  
Consequently, DFSS ultimately demonstrates itself as a real-time system that dynamically calculates the optimal percentage of feature sets for each sector over time. This is evidenced by the fact that the trends in importance do not vary, indicating that the system does not randomly determine these percentages.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sector**} & \multicolumn{2}{c}{**Win3**} & \multicolumn{2}{c}{**Win6**} & \multicolumn{2}{c}{**Win12**} & \multicolumn{2}{c}{**Win18**} \\ \cline{2-9}  & **1 st** & **1 st** & **1 st** & **1 st** & **1 st** & **1 st** & **1 st** & **1 st** \\  & **Algorithm** & **Score** & **Algorithm** & **Score** & **Algorithm** & **Score** & **Algorithm** & **Score** \\ \hline Energy & ERF (47) & 0.4938 & BSHAP (13) & 0.5014 & BC (20) & 0.5428 & ERF (20) & 0.5567 \\ Material & (34) & 0.5114 & BC (7) & 0.6503 & RFE (20) & 0.5188 & RFE (20) & 0.5824 \\ Industrial & RFE (20) & 0.4933 & PI (13) & 0.5076 & MI (20) & 0.5182 & \begin{tabular}{c} ELGBM \\ (20) \\ \end{tabular} & 0.5926 \\ Consumer & VT (7) & 0.4906 & ERF (13) & 0.4858 & BSHAP (20) & 0.5051 & PI (20) & 0.5426 \\ Consumer & MSURF & 0.4921 & SHAP (47) & 0.5032 & BSHAP (13) & 0.5349 & \begin{tabular}{c} MSURF \\ (13) \\ \end{tabular} & 0.5524 \\ Health Care & ELGBM (7) & 0.4777 & VT (47) & 0.4892 & SHAP (7) & 0.5214 & MI (20) & 0.5434 \\ Financial & PI (7) & 0.5403 & BSHAP(20) & 0.5322 & \begin{tabular}{c} ELASSO \\ (34) \\ \end{tabular} & 0.5511 & BSHAP (47) & 0.6153 \\ IT & MI (7) & 0.5215 & MI (7) & 0.5220 & VT (13) & 0.5467 & RR (20) & 0.5792 \\ Communication & BSHAP (20) & 0.4793 & RR (20) & 0.4995 & ELGBM (7) & 0.5467 & RFE (7) & 0.5689 \\ Devices & SHAP (20) & 0.4705 & \begin{tabular}{c} ELGBM \\ (20) \\ \end{tabular} & 0.5107 & \
\begin{tabular}{c} MSURF \\ (47) \\ \end{tabular} & 0.5467 &
\begin{tabular}{c} ELGBM \\ (47) \\ \end{tabular} & 0.5745 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dynamic adjustment of feature set across the timeline.  
Figure 4: Dynamic adjustment of the percentage of feature classes across timeline.  
Figure 5: Dynamic adjustment of the important features across timeline.