## 3 A Sample of the Experimental Work Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(r_{gran}\) & AccSG & SizeSG & AccCDG & SizeCDG \\ \hline
0.0769231 & 0.555556 & 1.2 & 0.804 & 2.2 \\
0.153846 & 0.44444 & 2.4 & 0.798 & 2.96 \\
0.230769 & 0.555556 & 3.2 & 0.799 & 5.12 \\
0.307692 & 0.777778 & 6.2 & 0.803 & 8.84 \\
0.384615 & 0.759259 & 11 & 0.819 & 16.76 \\
0.461538 & 0.833333 & 27 & 0.819 & 34.08 \\
0.538462 & 0.814815 & 58.4 & 0.824 & 71.68 \\
0.615385 & 0.814815 & 118 & 0.817 & 126.56 \\
0.692308 & 0.796296 & 177.8 & 0.827 & 180.92 \\
0.769231 & 0.814815 & 209.8 & 0.822 & 210 \\
0.846154 & 0.814815 & 216 & 0.826 & 216 \\
0.923077 & 0.814815 & 216 & 0.826 & 216 \\
1 & 0.814815 & 216 & 0.826 & 216 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Exemplary result for Standard vs. Concept-Dependent Granulation—5 times Cross Validation 5; Hepatitis 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. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Layer1**} & \multicolumn{2}{c}{**Layer2**} & \multicolumn{2}{c}{**Layer3**} & \multicolumn{2}{c}{**Layer4**} \\ \hline \(r_{gran}\) & **Acc** & **GranSize** & **Acc** & **GranSize** & **Acc** & **GranSize** & **Acc** & **GranSize** \\ \hline
0 & 0.768 & 2 & 0.768 & 2 & 0.768 & 2 & 0.768 & 2 \\
0.071 & 0.772 & 2 & 0.772 & 2 & 0.772 & 2 & 0.772 & 2 \\
0.143 & 0.696 & 2.6 & 0.774 & 2 & 0.774 & 2 & 0.774 & 2 \\
0.214 & 0.781 & 5.6 & 0.775 & 2 & 0.775 & 2 & 0.775 & 2 \\
0.286 & 0.8 & 6.8 & 0.797 & 2 & 0.797 & 2 & 0.797 & 2 \\
0.357 & 0.813 & 16.4 & 0.78 & 2 & 0.78 & 2 & 0.78 & 2 \\
0.429 & 0.838 & 29.6 & 0.704 & 3.6 & 0.67 & 2.2 & 0.67 & 2.2 \\
0.5 & 0.843 & 68.6 & 0.729 & 15.4 & 0.37 & 7.4 & 0.37 & 7.4 \\
0.571 & 0.851 & 154.8 & 0.799 & 70.6 & 0.69 & 47.4 & 0.628 & 43.2 \\
0.643 & 0.854 & 313.2 & 0.841 & 245.6 & 0.806 & 228.8 & 0.781 & 225.6 \\
0.714 & 0.852 & 468.2 & 0.854 & 444.8 & 0.855 & 440 & 0.857 & 438.6 \\
0.786 & 0.858 & 535.6 & 0.858 & 535.4 & 0.858 & 535.4 & 0.858 & 535.4 \\
0.857 & 0.854 & 547.4 & 0.854 & 547.4 & 0.854 & 547.4 & 0.854 & 547.4 \\
0.929 & 0.864 & 548.8 & 0.864 & 548.8 & 0.864 & 548.8 & 0.864 & 548.8 \\
1 & 0.855 & 552 & 0.855 & 552 & 0.855 & 552 & 0.855 & 552 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: CV-5; Result of experiments for multi-layer c-d granulation with use of kNN classifier; data set Australian credit; \(r_{gran}=\) Granulation radius, Acc = Average accuracy for the considered layer, GranSize The mean size of granular decision system for the considered layer. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Layer1**} & \multicolumn{2}{c}{**Layer2**} & \multicolumn{2}{c}{**Layer3**} & \multicolumn{2}{c}{**Layer4**} \\ \hline \(r_{gran}\) & **Acc** & **GranSize** & **Acc** & **GranSize** & **Acc** & **GranSize** & **Acc** & **GranSize** \\ \hline
0 & 0.315 & 4 & 0.315 & 4 & 0.315 & 4 & 0.315 & 4 \\
0.167 & 0.395 & 8.6 & 0.296 & 4 & 0.296 & 4 & 0.296 & 4 \\
0.333 & 0.484 & 16.4 & 0.351 & 6.2 & 0.326 & 4.6 & 0.326 & 4.6 \\
0.5 & 0.668 & 44 & 0.477 & 16.2 & 0.374 & 9.4 & 0.296 & 7 \\
0.667 & 0.811 & 102.8 & 0.723 & 47.4 & 0.632 & 29.8 & 0.601 & 25.4 \\
0.833 & 0.865 & 370 & 0.841 & 199.8 & 0.832 & 147.2 & 0.833 & 137 \\
1 & 0.944 & 1382.4 & 0.944 & 1382.4 & 0.944 & 1382.4 & 0.944 & 1382.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: CV-5; Result of experiments for multi-layer c-d granulation with use of kNN classifier; data set Car evaluation; \(r_{gran}=\) Granulation radius, Acc = Average accuracy for the considered layer, TRNsize The mean size of granular decision system for the considered layer.