## 3 Results
### Classification Results  
The ACC and NMI clustering results of nine datasets using the K-means algorithm are presented in Table 2. Bold values represent the best result under this dataset, while underscores indicate suboptimal outcomes. As evident from Table 2, it can be observed that DGA exhibits promising performance. Table 2 presents the results compared with several classic algorithms. Additionally, we compare with two newer algorithms. Following the same procedures as NLRL-LE ([10]) and LLSRFS ([40]), we repeated the k-means clustering 20 times and averaged the results to obtain the final clustering outcome. The results are shown in Table 3. It can be observed that, except for the WarpPIE10P and TOX-171 datasets, DGA outperforms the other two methods in terms of ACC across the remaining datasets. While DGA's overall NMI performance is not particularly outstanding, the differences in NMI values compared to the other two methods are minor. Overall, DGA exhibits a strong performance compared to the latest algorithms. Furthermore, we employed the Friedman test followed by the Nemenyi post hoc test to validate the performance of the proposed models, with the results presented in Table 4. At \(\alpha\) = 0.1, the Friedman test statistics for ACC and NMI metrics exceeded the critical value of 1.9006. Indicating significant differences in the performance among the compared algorithms. The Nemenyi post hoc test further discriminates these algorithmic differences. According to the Critical Difference (CD) diagram (Figure 3), we observe that apart from SCFS, DGA shows minimal overlap with other algorithms in terms of ACC and NMI. The pairwise rank differences with SCFS are 1.388 and 1.7778 for ACC and NMI, respectively, which are below the critical value of 2.742. Therefore, it is evident that DGA exhibits significantly different performance compared to the other algorithms, except for SCFS. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **All Features** & **LS** & **SCFS** & **UDFS** & **MCFS** & **DUFS** & **DGA** \\ \hline \multirow{6}{*}{ACC} & Isolet & 0.658 & 0.612 & 0.721 & 0.528 & 0.578 & 0.602 & **0.755** \\  & mnist & 0.519 & 0.469 & 0.5895 & 0.507 & 0.566 & 0.5575 & **0.6235** \\  & Yale (32*32) & 0.418 & 0.4667 & 0.442 & 0.442 & 0.406 & 0.419 & **0.527** \\  & ORL (32*32) & 0.5675 & 0.555 & 0.63 & 0.5275 & 0.62 & 0.58 & **0.645** \\  & COIL20 & 0.6889 & 0.617 & 0.731 & 0.637 & 0.707 & 0.686 & **0.739** \\  & WarpAR10P & 0.238 & 0.384 & 0.353 & 0.3 & 0.323 & 0.292 & **0.392** \\  & WarpPE10P & 0.2621 & 0.385 & 0.323 & 0.342 & 0.371 & **0.48** & 0.433 \\  & colon & 0.532 & 0.564 & **0.645** & 0.548 & 0.564 & 0.548 & 0.612 \\  & TOX-171 & 0.415 & 0.438 & 0.479 & 0.431 & 0.426 & **0.549** & 0.485 \\ \hline \multirow{6}{*}{NMI} & Isolet & 0.791 & 0.745 & 0.816 & 0.669 & 0.721 & 0.752 & **0.818** \\  & mnist & 0.458 & 0.361 & **0.513** & 0.441 & 0.453 & 0.5055 & 0.498 \\  & Yale (32*32) & 0.487 & 0.52 & 0.495 & 0.488 & 0.459 & 0.425 & **0.55** \\  & ORL (32*32) & 0.76 & 0.744 & 0.781 & 0.721 & 0.793 & 0.759 & **0.804** \\  & COIL20 & 0.791 & 0.744 & 0.792 & 0.747 & 0.782 & 0.751 & **0.803** \\  & WarpAR10P & 0.21718 & **0.438** & 0.304 & 0.254 & 0.338 & 0.278 & 0.38 \\  & WarpPE10P & 0.236 & 0.39 & 0.323 & 0.288 & 0.372 & **0.536** & 0.425 \\  & colon & 0.045797 & 0.06425 & 0.03363 & 0.03122 & 0.08478 & 0.03122 & **0.154** \\  & TOX-171 & 0.127 & 0.169 & 0.228 & 0.173 & 0.141 & **0.301** & 0.267 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The best accuracy (ACC) and normalized mutual information (NMI) of various feature selection algorithms. (The best and second-best outcomes are indicated by bold and underlined text, respectively). Among these datasets, optimal results are observed in six cases, whereas the remaining three datasets exhibit sub-optimal outcomes. Notably, when applied to Yale, the clustering accuracy of this algorithm improves by 12.9% in comparison to the second algorithm.