# Coverage-Based Classification Using Association Rule Mining +
## 6 Experimental Evaluation
Although J&B obtained a similar result with FR and CBA (there is no statistical difference on 8 datasets out of 12), it is beaten by the PT algorithm according to win/losses counts. However, on average, the classification accuracies of J&B are not much different from those of the other 8 rule-learners. Results show (Table 8) that J&B generates a reasonably smaller number of compact rules which is not sensitive to the size of the training datasets, while the number of rules produced by traditional classification methods such as \"DT\", \"C4.5\", \"PT\" and \"DTNB\" algorithms depend on the dataset's size. Even though not achieving the best classification accuracies on \"Nursery\" and \"Adult\" datasets, it produced the lowest number of rules on those datasets among all classification models. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Dataset** & **DTNB** & **DT** & **C4.5** & **PT** & **FR** & **RDR** & **CBA** & **SA** & **J\&B** & **OC** \\ \hline Breast.Can & \(70.4\pm 4.1\) & \(69.2\pm 6.7\) & \(75.0\pm 6.9\) & \(74.0\pm 4.0\) & \(75.1\pm 5.3\) & \(71.8\pm 5.7\) & \(71.9\pm 9.8\) & \(79.3\pm 4.4\) & \(80.5\pm 4.7\) & \(88.7\) \\ Balance & \(81.4\pm 8.1\) & \(66.7\pm 5.0\) & \(64.4\pm 4.3\) & \(76.2\pm 5.6\) & \(77.5\pm 7.6\) & \(68.5\pm 4.3\) & \(73.2\pm 3.8\) & \(74.0\pm 4.1\) & \(74.1\pm 2.6\) & \(86.3\) \\ Care.Ewn & \(95.4\pm 0.8\) & \(91.3\pm 1.7\) & \(92.1\pm 1.7\) & \(94.3\pm 1.0\) & \(91.8\pm 1.1\) & \(91.0\pm 1.8\) & \(91.2\pm 3.9\) & \(86.2\pm 2.1\) & \(89.4\pm 1.4\) & \(94.2\) \\ Vote & \(94.7\pm 3.4\) & \(94.9\pm 3.7\) & \(94.7\pm 4.4\) & \(94.8\pm 4.2\) & \(94.4\pm 2.8\) & \(95.6\pm 4.1\) & \(94.4\pm 2.6\) & \(94.7\pm 2.3\) & \(94.1\pm 1.8\) & \(92.8\) \\ Tic-Tac & \(69.9\pm 2.7\) & \(74.4\pm 4.4\) & \(85.2\pm 2.7\) & \(94.3\pm 3.3\) & \(94.1\pm 3.4\) & \(91.3\pm 2.9\) & \(100.0\pm 0.0\) & \(91.7\pm 1.5\) & \(95.8\pm 2.0\) & \(100.0\) \\ Nursery & \(94.0\pm 1.5\) & \(93.6\pm 1.2\) & \(95.4\pm 1.4\) & \(95.7\pm 1.7\) & \(91.0\pm 1.4\) & \(92.5\pm 1.5\) & \(92.1\pm 2.4\) & \(91.6\pm 1.2\) & \(89.6\pm 1.1\) & \(85.6\) \\ Hayes.R & \(75.0\pm 7.2\) & \(53.4\pm 8.3\) & \(78.7\pm 8.4\) & \(73.9\pm 7.7\) & \(78.7\pm 7.8\) & \(74.3\pm 7.1\) & \(75.6\pm 10.9\) & \(73.1\pm 6.0\) & \(79.3\pm 5.9\) & \(80.7\) \\ Lymph & \(72.9\pm 9.0\) & \(72.2\pm 8.3\) & \(76.2\pm 8.7\) & \(81.7\pm 9.0\) & \(80.0\pm 8.2\) & \(78.3\pm 7.3\) & \(79.0\pm 9.7\) & \(73.7\pm 5.1\) & \(80.6\pm 5.7\) & \(90.1\) \\ Spect.H & \(73.9\pm 2.7\) & \(79.3\pm 1.6\) & \(60.9\pm 9.0\) & \(80.4\pm 5.6\) & \(80.4\pm 2.2\) & \(79.0\pm 1.6\) & \(79.1\pm 2.1\) & \(79.7\pm 3.1\) & \(94.8\) \\ Adult & \(73.0\pm 4.1\) & \(82.0\pm 2.3\) & \(82.4\pm 4.7\) & \(82.1\pm 4.7\) & \(75.2\pm 3.0\) & \(80.8\pm 2.7\) & \(81.8\pm 3.4\) & \(80.8\pm 2.6\) & \(80.8\pm 2.9\) & \(91.7\) \\ Chess & \(93.7\pm 3.0\) & \(97.3\pm 3.1\) & \(98.9\pm 3.6\) & \(98.9\pm 3.1\) & \(96.4\pm 2.1\) & \(95.8\pm 3.3\) & \(95.4\pm 2.9\) & \(92.2\pm 3.8\) & \(94.6\pm 2.7\) & \(100.0\) \\ Connect4 & \(78.8\pm 5.9\) & \(76.7\pm 7.7\) & \(80.0\pm 6.8\) & \(81.1\pm 7.9\) & \(80.6\pm 7.1\) & \(80.0\pm 6.4\) & \(80.9\pm 8.1\) & \(78.7\pm 6.0\) & \(81.2\pm 5.2\) & \(90.6\) \\ Avg (\%): & \(81.5\pm 4.4\) & \(79.3\pm 4.5\) & \(83.6\pm 5.2\) & \(85.6\pm 4.9\) & \(84.5\pm 4.4\) & \(83.6\pm 4.1\) & \(84.5\pm 4.9\) & \(82.3\pm 3.4\) & \(84.9\pm 3.3\) & \(91.3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The comparison between our method and some classification algorithms on accuracy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **DTNB** & **DT** & **C4.5** & **PT** & **FR** & **RDR** & **CBA** & **SA** \\ \hline
**W** & 6 & 6 & 4 & 2 & 2 & 4 & 2 & 6 \\
**L** & 3 & 2 & 3 & 4 & 2 & 1 & 2 & 1 \\
**W-L** & 3 & 4 & 5 & 6 & 8 & 7 & 8 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistically significant wins/losses counts of J&B method on accuracy.