# Enhanced Feature Selection via Hierarchical Concept Modeling
## 5 Results and Discussion
### The Hierarchical Concept Model Performance
#### 5.2.2 Feature Selection Using FCA  
FCA's ability to create a structured concept lattice enables it to systematically reduce the number of features while retaining classification robustness. Within the concept lattice, FCA organizes features according to their generalization and specialization relationships, which helps distinguish between essential and redundant attributes. Features that consistently contribute to accurate classification outcomes are preserved within the hierarchical structure, while those that do not support the classification process are pruned. This lattice-based hierarchy allows FCA to efficiently identify a minimal yet comprehensive feature subset that captures the necessary information for classification. As a result, the feature set derived using FCA maintains a level of classification accuracy comparable to the original dataset while achieving significant dimensionality reduction. This is particularly beneficial in large datasets, where FCA's structure can simplify models, reducing computational load without compromising accuracy. Figure 4: The comparison DT-SVM-ANN classifiers with features selected using DT from the first to the fifth level and with all original features. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{10}{c}{**The Classification Accuracy (\%) Using Decision Tree-Based Feature Selection**} \\ \cline{2-13}
**Dataset** & \multicolumn{4}{c}{**LV:1**} & \multicolumn{4}{c}{**LV:2**} & \multicolumn{4}{c}{**LV:3**} \\ \cline{2-13}  & \multicolumn{2}{c}{**No. A-**} & \multicolumn{2}{c}{**No. A-**} & \multicolumn{2}{c}{**Ulphone**} & \multicolumn{2}{c}{**DT**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**No. A-**} & \multicolumn{2}{c}{**DT**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**No. A-**} & \multicolumn{2}{c}{**DT**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**Attribute**} & \multicolumn{2}{c}{**DT**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**ANN**} \\ \cline{2-13}  & \multicolumn{2}{c}{**No. A-**} & \multicolumn{2}{c}{**tribube**} & \multicolumn{2}{c}{**DT**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**tribube**} & \multicolumn{2}{c}{**DT**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**tribube**} & \multicolumn{2}{c}{**DT**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**Attribute**} & \multicolumn{2}{c}{**DT**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**ANN**} \\ \hline Dermatology & 1 & 66.94 & 66.95 & 66.94 & 3 & 73.77 & 75.15 & 73.50 & 5 & 73.50 & 5 & 73.50 & 6 & 89.65 & 89.65 & 89.65 & 8 & 96.16 & 96.16 & 96.16 \\ \hline Glass & 1 & 100.00 & 82.24 & 99.53 & 3 & 100.00 & 85.19 & 1 The other hierarchical method, FCA, was then applied to generate the hierarchical construct. We applied the ConExp program to build the concept lattice structure. Similarly, we selected the attributes from each level of concept lattice from the first to the fifth level. The classification accuracy results are shown in Table 5. From this table, we generate the color-coded heatmap showed as Figure 5 showing classification accuracy across various levels (LV.1 to LV.5) for different classifiers (DT, SVM, ANN) using FCA-based feature selection. This visualization highlights the classification performance across datasets, with darker shades representing higher accuracy, facilitating a quick comparison across levels and classifiers. The results show that as the levels increase from LV.1 to LV.5, certain datasets, such as Pageblocks, Movement, and Tunadromd, show improvements in classification accuracy. This pattern suggests that higher levels in the FCA hierarchy incorporate additional relevant features, enhancing the classifiers' ability to make accurate predictions.