## 4 Application of Selected Other Classifiers on Granular Data
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{8}{c}{**Acc**} & \multicolumn{8}{c}{**GranSize**} \\ \hline \(r_{gran}\) & \(V1\) & \(V2\) & \(V3\) & \(V4\) & \(V1\) & \(V2\) & \(V3\) & \(V4\) \\ \hline
0.0769231 & 0.801 & 0.774 & 0.785 & 0.793 & 2.04 & 2.2 & 2.12 & 2.16 \\
0.153846 & 0.802 & 0.752 & 0.773 & 0.781 & 2.68 & 3.08 & 2.96 & 2.88 \\
0.230769 & 0.807 & 0.736 & 0.731 & 0.758 & 4.56 & 4.96 & 4.72 & 4.56 \\
0.307692 & 0.802 & 0.784 & 0.722 & 0.735 & 9.2 & 8.28 & 8.52 & 9 \\
0.384615 & 0.824 & 0.806 & 0.79 & 0.79 & 16.6 & 16.04 & 16.48 & 16.72 \\
0.461538 & 0.823 & 0.824 & 0.763 & 0.753 & 34.84 & 34.64 & 34.36 & 35.32 \\
0.538462 & 0.841 & 0.814 & 0.722 & 0.709 & 69.44 & 70.2 & 69.44 & 70.32 \\
0.615385 & 0.827 & 0.814 & 0.696 & 0.707 & 127.24 & 127.2 & 126.76 & 127.8 \\
0.692308 & 0.83 & 0.821 & 0.73 & 0.727 & 181.36 & 181.28 & 181.28 & 180.28 \\
0.769231 & 0.83 & 0.796 & 0.738 & 0.737 & 210.56 & 210.12 & 210.24 & 210.36 \\
0.846154 & 0.829 & 0.776 & 0.739 & 0.739 & 216 & 216 & 216 & 216 \\
0.923077 & 0.829 & 0.776 & 0.739 & 0.739 & 216 & 216 & 216 & 216 \\
1 & 0.829 & 0.776 & 0.739 & 0.739 & 216 & 216 & 216 & \\ \hline \end{tabular}
\end{table} 
Table 21: \(5\times\) CV-5; The result of experiments for four variants of the Naive Bayes classifier; data set Australian credit; concept dependent granulation; \(r_{gran}=\) Granulation radius; nil = result for data without missing values; Acc = Accuracy of classification; GranSize = The size of data set after granulation in the fixed \(r\). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{8}{c}{**Acc**} & \multicolumn{8}{c}{**GranSize**} \\ \hline \(r_{gran}\) & \(V1\) & \(V2\) & \(V3\) & \(V4\) & \(V1\) & \(V2\) & \(V3\) & \(V4\) \\ \hline
0.166667 & 0.315 & 0.653 & 0.092 & 0.369 & 8.12 & 8.48 & 7.72 & 8.52 \\
0.333333 & 0.357 & 0.723 & 0.044 & 0.118 & 17.96 & 17.44 & 17.36 & 17.4 \\
0.5 & 0.383 & 0.715 & 0.077 & 0.32 & 38.96 & 38.52 & 36.72 & 38.84 \\
0.666667 & 0.403 & 0.7 & 0.108 & 0.382 & 105.28 & 106.12 & 106.84 & 107.32 \\
0.833333 & 0.436 & 0.7 & 0.06 & 0.328 & 368.88 & 369.08 & 369.28 & 374.68 \\
1 & 0.451 & 0.7 & 0.052 & 0.196 & 1382.4 & 1382.4 & 1382.4 & 1382.4 \\ \hline \end{tabular}
\end{table} 
Table 22: \(5\times\) CV-5; The result of experiments for four variants of the Naive Bayes classifier; data set Car evaluation; concept dependent granulation; \(r_{gran}=\) Granulation radius; nil = result for data without missing values; Acc = Accuracy of classification; GranSize = The size of data set after granulation in the fixed \(r\). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{8}{c}{**Acc**} & \multicolumn{8}{c}{**GranSize**} \\ \hline \(r_{gran}\) & \(V1\) & \(V2\) & \(V3\) & \(V4\) & \(V1\) & \(V2\) & \(V3\) & \(V4\) \\ \hline
0.0769231 & 0.801 & 0.774 & 0.785 & 0.793 & 2.04 & 2.2 & 2.12 & 2.16 \\
0.153846 & 0.802 & 0.752 & 0.773 & 0.781 &  
In Table 25, we have presented an example of the result of a deep neural network on the granulated data--see [39]. It turns out that it learns the internal knowledge of decision-making systems and maintains a high level of classification effectiveness. In Table 25 and Figure 4, we have the result for Australian Credit data set, for radius 0.66, with a reduction of 40 percent, and classification efficiency is around 84 percent.