## 3 A Sample of the Experimental Work Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Attr No.** & **Obj No.** & **Class No.** \\ \hline Australian-credit & 15 & 690 & 2 \\ Car Evaluation & 7 & 1728 & 4 \\ Heartdisease & 14 & 270 & 2 \\ Hepatitis & 20 & 155 & 2 \\ Wisconsin Diagnostic Breast Cancer & 32 & 569 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Exemplary decision systems from UCI Machine Learning Repository. Australian credit, Car Evaluation, Heartdisease, and Hepatitis were used in the comparison of standard and concept-dependent granulation with a kNN Classifier. Comparing homogeneous variants with a kNN Classifier, we did not use the car system in the epsilon variant because it is symbolic. We used all four systems to present the effectiveness with the Classifier. To present the effectiveness with the SVM classifier, we used a Wisconsin Diagnostic Breast Cancer system [37]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(r_{gran}\) & **AccSG** & **SizeSG** & **AccCDG** & **SizeCDG** \\ \hline
0.071428 & 0.444928 & 2.36 & 0.773 & 2.64 \\
0.142857 & 0.444928 & 5.12 & 0.779 & 3.92 \\
0.214286 & 0.821739 & 4.76 & 0.786 & 5.36 \\
0.285714 & 0.84058 & 4.8 & 0.804 & 9.12 \\
0.357143 & 0.768116 & 9.4 & 0.813 & 16.12 \\
0.428571 & 0.775362 & 24.2 & 0.828 & 32.44 \\
0.5 & 0.847826 & 51.2 & 0.845 & 71.64 \\
0.571429 & 0.818841 & 133.4 & 0.838 & 157.96 \\
0.642857 & 0.833333 & 297 & 0.845 & 318.96 \\
0.714286 & 0.811594 & 455.2 & 0.854 & 468.16 \\
0.785714 & 0.855072 & 533.2 & 0.858 & 535.84 \\
0.857143 & 0.826087 & 546.4 & 0.861 & 547.2 \\
0.928571 & 0.826087 & 547.8 & 0.863 & 548.8 \\
1 & 0.826087 & 552 & 0.861 & 552 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Exemplary result for Standard vs. Concept-Dependent Granulation—5 times Cross Validation 5; Australian Credit data set; \(r_{gran}=\) Granulation radius, AccSG = Accuracy of classification for Standard Granulation, AccDG—Accuracy for Concept-Dependent Granulation, SizeSG = Granular decision system size for Standard Granulation, SizeCDG = Granular decision system size for Concept-Dependent Granulation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(r_{gran}\) & **AccSG** & **SizeSG** & **AccCDG** & **SizeCDG** \\ \hline
0.167 & 0.388988 & 8.08 & 0.396 & 8.32 \\
0.333 & 0.456468 & 17.16 & 0.539 & 16.96 \\
0.500 & 0.495127 & 38.84 & 0.681 & 38.2 \\
0.667 & 0.546064 & 106.24 & 0.804 & 107.04 \\
0.833 & 0.611924 & 368.76 & 0.864 & 371.64 \\
1.000 & 0.359964 & 1382.4 & 0.944 & 1382.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Exemplary decision systems from UCI Machine Learning Repository. Australian credit, Car Evaluation, Heartdisease, and Hepatitis were used in the comparison of standard and concept-dependent granulation with a kNN Classifier. Comparing homogeneous variants with a kNN Classifier, we did not use the car system in the epsilon variant because it is symbolic. We used all four systems to present the effectiveness with the Classifier. To present the effectiveness with the SVM classifier, we used a Wisconsin Diagnostic Breast Cancer system [37]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(r_{gran}\) & AccSG & SizeSG & AccCDG & SizeCDG \\ \hline  
& 0.895 & 124 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Exemplary result for Standard vs. Concept-Dependent Granulation—5 times Cross Validation 5; Heart Disease data set; \(r_{gran}=\) Granulation radius, AccSG = Accuracy of classification for Standard Granulation, AccCDG—Accuracy for Concept-Dependent Granulation, SizeSG = Granular decision system size for Standard Granulation, SizeCDG = Granular decision system size for Concept-Dependent Granulation.