## 3 Experiment Results
### Analysis of Results  
This section aims to demonstrate the effectiveness of the proposed algorithm by comparing it with existing filter methods. Firstly, we analyze and evaluate the results of the proposed algorithm for the five target classes on the three datasets. Table 3 presents the average results obtained using 10-fold cross-validation for the proposed algorithm. It should be noted that datasets BPSnp_1, BPSnp_2, and BPSnp_3 refer to the BPSnp dataset, with body size and weight, muscle-fat ratio, and growth rate as target classes, respectively. The results in Table 3 reflect the performance of the classification models with the proposed FS algorithm applied.  
As shown in Table 3, the feature subsets selected by the proposed algorithm exhibit excellent data quality, as they demonstrate maximum relevance to the target class and lack any redundant features. This implies that all of the feature subsets belong to MRNR feature subsets. Notably, the algorithm's effectiveness is highlighted by its ability to select a small number of features while still achieving relatively high classification accuracy across all four ML algorithms. This success in accurately classifying the data further attests to the quality of the selected features.  
Further analysis of classification accuracy reveals that the accuracy of RF is higher than that of other ML algorithms, primarily because RF is a more complex ML algorithm  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Hyperparameters** & **Values** \\ \hline NB & - & No additional tuning \\ \hline SVM & \begin{tabular}{c} C \\ tol \\ kernel \\ \end{tabular} & \begin{tabular}{c} 1 \\ 0.001 \\ rbf \\ \end{tabular} \\ \hline C4.5 & \begin{tabular}{c} Min\_samples\_leaf \\ Confidence\_factor\_for\_pruning \\ \end{tabular} & \begin{tabular}{c} 5 \\ 0.25 \\ \end{tabular} \\ \hline RF & \
\begin{tabular}{c} n\_estimators \\ max\_depth \\ min\_samples\_split \\ random\_state \\ \end{tabular} &
\begin{tabular}{c} 100 \\ None \\ 2 \\ 42 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings of ML algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Rel** & **NRF** & **FN** &
\begin{tabular}{c} **Execution** \\ **Time (s)** \\ \end{tabular} & \multicolumn{4}{c}{**OA**} \\ \cline{6-9}  & **NB** & **SVM** & **C4.5** & **RF** \\ \hline BPSx & 1 & 0 & 8 & 44.74 & 0.887 & 0.904 & 0.844 & **0.910** \\ BPSnp\_1 & 1 & 0 & 9 & 62.21 & 0.925 & 0.923 & 0.918 & **0.946** \\ BPSnp\_2 & 1 & 0 & 9 & 68.16 & 0.922 & 0.933 & 0.931 & **0.942** \\ BPSnp\_3 & 1 & 0 & 8 & 62.49 & 0.928 & 0.940 & 0.932 & **0.946** \\ BPPRRS & 1 & 0 & 6 & 38.47 & 0.954 & 0.968 & 0.959 & **0.970** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of the proposed algorithm.  
that utilizes multiple classifiers compared to others. However, it is noteworthy that NB, SVM, and C4.5 have also achieved high classification accuracy, which is close to that of RF. This observation highlights that the features selected by the proposed FS algorithm possess very high quality and effectively mitigate the impact of classifiers on accuracy.  
Additionally, the algorithm's performance is measured positively, as evidenced by its good results in terms of feature stability and running time. In conclusion, these evaluations affirm the effectiveness of the proposed algorithm in identifying high-quality feature subsets for data classification. In the subsequent subsections, we will compare the results of the proposed algorithm with those of existing filter methods.