## 4 Experimental Evaluation  
Empirical evaluations were performed on 12 real-life datasets taken from the UCI ML Repository. The developed associative classifiers were compared with 6 associative classifiers (CBA and our previously developed classifiers: SA, J&B, DC, DDC, and CDC) on accuracy and size. We also included the Majority Class Classifier (MCC) in the comparison to see if AC algorithms have problems classifying minority classes. The new associative classifiers were compared with the above-mentioned AC methods because in previous research [28; 29; 31], a comprehensive evaluation of our above-mentioned AC methods with well-known rule-based classification models was already provided. This study utilized a paired _t_-test (with a 95% significance threshold) method of statistical significance testing to present the statistical differences in the obtained results. A paired _t_-test is used to compare the means of two sample sets when each observation in one sample could be paired with an observation in the other sample. In our case, two sample sets are the classification accuracies of two models over a 10-fold cross-validation method (10 pairs of results, a pair of results for each fold). The same default parameters were applied for all AC methods; to make a fair comparison, _min_support_, and _min_confidence_ thresholds were set to 1% and 60%, respectively, as default parameters. In some datasets, _minimum support_ was lowered to 0.5% or even _0.1%_, and confidence was lowered to _50%_ to ensure \"enough\" CARs (\"enough\" means at least 5-10 rules for each class value; this situation mainly happens with imbalanced datasets) were generated for each class value. We utilized the WEKA workbench [42] implementation of all associative classifiers. A 10-fold cross-validation evaluation protocol was used to perform the experiments. The descriptions of the datasets and input parameters of the AC methods are shown in Table 1.  
Table 2 illustrates the experimental results on classification accuracies (average values over the 10-fold cross-validation with standard deviations). The best accuracy for each dataset was shown in bold. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **\# of** **\#** **\#** **\#** **Classes** & **Class Distribution** & **\# of Records** & **Min Support** & **Min Confidence** & **\# of** **Analyzed Rules** \\ \hline Breast.Can & 10 & 2 & 201; 85 & 286 & 1\% & 60\% & 1000 \\ \hline Balance & 5 & 3 & 288; 49; 288 & 625 & 1\% & 50\% & 218 \\ \hline Car.Evn & 7 & 4 & \begin{tabular}{c} 1210; 384; 69; \\ 65 \\ \end{tabular} & 1728 & 1\% & 50\% & 1000 \\ \hline Vote & 17 & 2 & 267; 168 & 435 & 1\% & 60\% & 500 \\ \hline Tic-Tac & 10 & 2 & 626; 332 & 958 & 1\% & 60\% & 3000 \\ \hline Nursery & 9 & 5 & \
\begin{tabular}{c} 4320; 2; 328; \\ 4266; 4044 \\ \end{tabular} & 12,960 & 0.5\% & 50\% & 3000 \\ \hline Hayes.R & 6 & 3 & 65; 64; 31 & 160 & 0.1\% & 50\% & 1000 \\ \hline Lymp & 19 & 4 & 2; 81; 61; 4 & 148 & 1\% & 60\% & 1500 \\ \hline Spect.H & 23 & 2 & 56; 211 & 267 & 0.5\% & 50\% & 3000 \\ \hline Adult & 15 & 2 & 34,013; 11,208 & 45,221 & 0.5\% & 60\% & 5000 \\ \hline Chess & 37 & 2 & 2839; 357 & 3196 & 0.5\% & 60\% & 3000 \\ \hline Connect4 & 43 & 3 &
\begin{tabular}{c} 44,473; \\ 16,635; 6449 \\ \end{tabular} & 67,557 & 1\% & 60\% & 5000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of datasets and AC algorithm parameters. Overall observations on accuracy (shown in Table 2) reveal that newly developed techniques CCC1, CCC2, and CCC3 achieved an average accuracy that was comparable to the other associative classifiers (84.7%, 84.9%, and 84.1% respectively). The average accuracy of CCC2 was higher than the CCC1 and CCC3 approaches (and higher than CBA, SA, DC, and DDC as well), while the J&B and CDC algorithms produced results that were similar or slightly higher. Noticeably, the proposed methods achieved the best accuracy on the \"Balance\" (CC1: 75.9%), \"Nursery\" (CCC2: 92.9%), and \"Connect4\" (CCC2: 81.7%) datasets among all AC methods.