## 6 Conclusions  
In this paper, a new unsupervised feature selection method, densest feature graph augmentation with disjoint feature clusters (DFG-A-DFC), has been proposed. The proposed method represents the feature set as a graph with the dissimilarity between features  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Algorithm** & **SVM** & **Naive Bayes** & **KNN** & **Adaboost** \\ \hline \multirow{2}{*}{Colon} & First Phase & 74.04(0.82) & 57.61(1.65) & 78.33(2.13) & 52.14(1.53) \\  & Second Phase & 86.90(1.44) & 61.42(1.57) & 81.90(1.57) & 56.42(1.81) \\ \hline \multirow{2}{*}{Multiple Features} & First Phase & 97.44(0.05) & 87.79(0.23) & 92.15(0.16) & 83.44(0.26) \\  & Second Phase & 98.60(0.07) & 96.00(0.12) & 96.24(0.14) & 95.55(0.13) \\ \hline \multirow{2}{*}{Isolet} & First Phase & 33.50(0.21) & 21.00(0.13) & 31.66(0.14) & 18.78(0.19) \\  & Second Phase & 97.06(0.05) & 79.99(0.13) & 88.40(0.12) & 80.33(0.13) \\ \hline \multirow{2}{*}{Spambase} & First Phase & 79.24(0.13) & 55.05(0.24) & 55.98(0.24) & 79.04(0.11) \\  & Second Phase & 93.65(0.06) & 80.06(0.13) & 86.22(0.12) & 82.67(0.244) \\ \hline \multirow{2}{*}{Ionosphere} & First Phase & 93.72(0.25) & 87.15(0.72) & 82.61(0.53) & 89.15(0.51) \\  & Second Phase & 95.73(0.36) & 89.47(0.60) & 84.02(0.99) & 90.31(0.38) \\ \hline \multirow{2}{*}{WDBC} & First Phase & 82.08(0.62) & 77.32(0.43) & 80.67(0.37) & 81.18(0.26) \\  & Second Phase & 97.77(0.01) & 91.73(0.43) & 95.95(0.02) & 95.78(0.27) \\ \hline \multirow{2}{*}{Sonar} & First Phase & 64.45(0.57) & 65.30(1.00) & 62.50(0.84) & 63.50(0.86) \\  & Second Phase & 83.59(1.00) & 71.21(0.84) & 72.04(0.99) & 81.85(0.91) \\ \hline \multirow{2}{*}{SPECTF} & First Phase & 70.00(1.39) & 78.75(0.80) & 64.50(1.39) & 70.00(1.39) \\  & Second Phase & 80.00(1.39) & 80.00(1.39) & 67.50(0.16) & 82.50(0.99) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study of the proposed method in terms of ACC parameter.  
Figure 4: Comparison of the number of selected features by considered method.  
as the edge weights. In the first phase, the features selected in the densest subgraph are considered the initial feature subset. In the second phase, shared nearest-neighbor-based clustering is applied to the feature set. Lastly, the final feature subset is formed from the augmentation of the initial feature subset with representative features from the formed clusters. To validate the efficiency of the proposed method, eight UCI datasets have been considered and compared against four existing unsupervised feature selection methods in terms of two performance criteria, namely classification accuracy and Mathews correlation coefficient. Experiments demonstrate that the proposed method is an efficient method in reducing the number of features along with better performance. Thus, it can be used as an alternative for performing feature selection. In the future, the proposed method can be applied to real-time applications such as image segmentation, wireless sensor, and data mining. Furthermore, the proposed method can be extended to big data.  
Conceptualization, D.C. and A.S.; Data curation, D.C., R.C. and H.M.; Formal analysis, D.C., H.M., R.C. and E.Y.; Funding acquisition, A.S. and M.P.; Methodology, D.C., A.S., H.M. and R.C.; Project administration, E.Y. and M.P.; Resources, A.S. and M.P.; Software, D.C., H.M. and R.C.; Supervision, A.S. and M.P.; Validation, H.M. and M.P.; Visualization, D.C., R.C. and E.Y.; Writing--original draft, D.C. and H.M.; Writing--review and editing, D.C., H.M., A.S., R.C., E.Y. and M.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data is publicly available and can be found here, [[http://archive.ics.uci.edu/ml](http://archive.ics.uci.edu/ml)]([http://archive.ics.uci.edu/ml](http://archive.ics.uci.edu/ml)) (accessed on 22 October 2022).  
The authors declare no conflict of interest.