## 4 Experimental Evaluation
Surprisingly, the developed methods produced unexpectedly large classifiers on the \"Balance\" and \"Hayes.R\" datasets. Overall, CCC1, CCC2, and CCC3 produced classifiers similar to other AC methods included in the study on size. An interesting and expected fact is that if we increase the \(\alpha\) parameter (that means that the majority of the \"Combined\" distance metric becomes the \"Indirect\" distance metric), we get the higher average number of rules with slightly better average accuracy. If we decrease the \(\alpha\) parameter (that means that the majority of the \"Combined\" distance metric becomes the \"Direct\" distance metric), we get the lower average number of rules with slightly worse average accuracy. The statistical significance testing (win/loss counts) results are shown in Tables 7-9 for each proposed classifier. Statistically significant counts (wins/losses) of CCC1 against other rule-based classification models on classification rules are shown in Table 7.  
In terms of number of rules, our suggested classifiers provided results comparable to cluster-based AC methods (DC, DDC, CCC3), but these results were better than those of other AC algorithms (CBA, SA, J&B). On the importance of win/loss counts, CCC1 \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **CBA** & **SA** & **J\&B** & **DC\&DDC** & **CDC\&CC1** & CCC2 & **CCC3** \\ \hline Breast.Can & 63 & 20 & 47 & **8** & 9 & 11 & 9 \\ Balance & 77 & 45 & 79 & **34** & 79 & 86 & 61 \\ Car.Evn & 72 & 160 & 41 & 32 & 32 & 37 & 32 \\ Vote & 22 & 30 & 13 & 6 & 6 & 6 & 9 \\ Tic-Tac & 23 & 60 & **14** & 24 & 17 & 27 & 21 \\ Nursery & 141 & 175 & 109 & 79 & 80 & 114 & **74** \\ Hayes.R & 34 & 45 & 34 & **19** & 80 & 80 & 37 \\ Lymp & 23 & 60 & 29 & 5 & 7 & 6 & 5 \\ Spect.H & **4** & 50 & 11 & 8 & 5 & 7 & 6 \\ Adult & 126 & 130 & 97 & **13** & 88 & 101 & 79 \\ Chess & 12 & 120 & 24 & **12** & 17 & 14 & 14 \\ Connect4 & 349 & 600 & 273 & **59** & 102 & 146 & 117 \\ \hline
**Average:** & 79 & 125 & 64 & 25 & 43 & 53 & 39 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Number of CARs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **CBA** & **SA** & **J\&B** & **DC\&DDC** & **CCC3** \\ \hline
**W** & 8 & 10 & 9 & 2 & 6 & 3 \\ L & 2 & 2 & 2 & 5 & 1 & 5 \\
**N** & 2 & 0 & 1 & 5 & 5 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistically significant wins/losses counts of CCC1 method on number of rules. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CBA** & **SA** & **J\&B** & **DC\&DDC** & **CCC3** \\ \hline
**W** & 7 & 10 & 7 & 0 & 1 \\
**L** & 4 & 2 & 5 & 7 & 7 \\
**N** & 1 & 0 & 0 & 5 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistically significant wins/losses counts of CCC2 method on the number of rules. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **CBA** & **SA** & **J\&B** & **DC\&DDC** \\ \hline
**W** & 8 & 11 & 10 & 2 \\
**L** & 1 & 1 & 2 & 5 \\
**N** & 3 & 0 & 0 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Statistically significant wins/losses counts of CCC3 method on the number of rules. produced significantly smaller classifiers than CBA, SA, J&B, and CCC2 on more than half of the datasets but did not perform as well as DC, DDC, and CCC3 algorithms (it generated the larger classifier on 5 out of the 12 datasets). The CCC2 classifier performed the worst among the newly developed methods (CCC1, CCC3) on size. Although CCC2 produced larger classifiers than DC, DDC, and CCC3 on 7 datasets out of 12, it achieved a better result than CBA, SA, and J&B methods according to significant wins/losses counts. Table 9 represents that CCC3 outperformed all AC methods except DC and DDC in terms of classification rules. It generated a larger classifier on only 1 dataset than CBA, SA, and 2 datasets out of 12 than J&B.