# Enhanced Feature Selection via Hierarchical Concept Modeling
## 5 Results and Discussion
### Comparison of Performances  
We compared the classification accuracies of our feature selection using decision trees in Section 5.2 with others in Section 5.3, as illustrated in Figure 11. The figure depicts the classification performance after utilizing different numbers of attributes at each level (from Level 1 to 3, from top to bottom respectively) for each classification method denoted as Figure 11a-c, respectively. Detailed data can be found in Tables 4 and 7. From the graphs in each dataset, considering the accuracy of attribute selection at each level, it appears that the performance is relatively consistent with other feature selection methods. For some datasets, the presented method may exhibit superior accuracy from the initial level, such as the Soybean dataset and Segmentation dataset. Conversely, the presented method may have lower accuracy for certain datasets, as observed in the Dermatology dataset. Similarly, we conducted a comparison of the classification accuracy of our models using FCA in Section 5.2 with others in Section 5.3, illustrated in Figure 12. The figure demonstrates the classification performance after employing varying numbers of attributes at each level, with detailed data available in Tables 5 and 7. Upon examining the accuracy trends from this graph, we observe a similar pattern to use the decision tree approach, \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**IG**} & \multicolumn{4}{c}{**CS**} & \multicolumn{4}{c}{**FS**} & \multicolumn{4}{c}{**BS**} \\ \cline{2-13}  & **DT** & **SVM** & **ANN** & **No.** & **DT** & **SVM** & **ANN** & **No.** & **DT** & **SVM** & **ANN** & **No.** & **DT** & **SVM** & **ANN** & **No.** \\ \hline Dermatology & 92.35 & 93.00 & 92.35 & 12 & 86.07 & 87.29 & 86.07 & 7 & 85.79 & 87.85 & 84.64 & 9 & 85.79 & 87.85 & 85.79 & 33 \\ \hline Glass & 99.53 & 100.00 & 100.00 & 4 & 99.53 & 99.82 & 100.00 & 3 & 100.00 & 100.00 & 100.00 & 9 & 100.00 & 100.00 & 100.00 & 3 \\ \hline Iris & 95.33 & 95.33 & 95.00 & 2 & 95.33 & 94.22 & 95.33 & 2 & 96.00 & 96.04 & 96.00 & 1 & 96.00 & 96.04 & 96.67 & 3 \\ \hline Lung-cancer & 68.50 & 68.50 & 69.00 & 2 & 87.50 & 86.50 & 86.50 & 16 & 87.50 & 87.50 & 83.50 & 55 & 87.50 & 87.50 & 87.50 & 2 \\ \hline Movement & 66.64 & 66.65 & 65.45 & 16 & 75.00 & 74.51 & 76.02 & 34 & 76.94 & 82.50 & 77.85 & 88 & 79.44 & 82.50 & 83.33 & 6 \\ \hline Pageblocks & 97.92 & 97.92 & 97.80 & 4 & 98.19 & 97.63 & 98.02 & 5 & 98.14 & 98.06 & 97.94 & 8 & 98.32 & 98.12 & 98.06 & 4 \\ \hline Segmentation & 90.91 & 89.33 & 90.87 & 10 & 88.61 & 88.61 & 88.52 & 7 & 89.22 & 89.48 & 80.78 & 6 & 89.48 & 89.52 & 97.36 & 10 \\ \hline Soybean & 97.87 & 100.00 & 98.87 & 14 & 100.00 & 100.00 & 98.87 & 10 & 100.00 & 100.00 & 97.87 & 2 & 100.00 & 100.00 & 100.00 & 34 \\ \hline Wine & 82.58 & 82.50 & 82.00 & 5 & 83.15 & 83.15 & 84.22 & 6 & 85.96 & 88.22 & 90.45 & 9 & 87.08 & 88.48 & 89.89 & 10 \\ \hline Zoo & 99.00 & 99.50 & 100.00 & 8 & 95.09 & 96.56 & 96.00 & 4 & 96.04 & 96.08 & 100.00 & 6 & 96.04 & 96.08 & 96.04 & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The classification performances of prior feature selection approaches. where some datasets may exhibit comparable performance to other methods, such as the Movement dataset, while others may show comparatively lower classification accuracy. From the above results, the outcomes obtained by employing a hierarchical structure for feature selection before the classification task indicate that it could lead to either favorable or less favorable results. This corresponds to the observed patterns seen in other feature selection methods used for comparison. Importantly, the classification performance of the proposed method does not consistently appear inferior. However, the presented key point in this study is its ability to significantly reduce the number of attributes compared to previous methods.