# Efficient Rule Generation for Associative Classification
## 5 Experimental Setting and Result
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Data Sets** & **CBA** & **CMAR** & **FACA** & **ECARG** & **ECARG2** \\ \hline Anneal & 73.47 & 29.16 & 10.78 & **10.68** & 13.38 \\ Breast & 25.44 & 23.96 & 24.4 & **1.92** & 3.54 \\ Cars & 60.08 & 21.17 & 8.98 & **3.05** & 3.76 \\ Contact & 2.65 & **0.99** & 1.87 & 1.78 & 1.84 \\ Diabetes & 28.08 & 26.74 & 24.61 & **3.01** & 7.30 \\ Iris & 4.16 & 2.40 & 1.88 & **1.17** & **1.17** \\ Labor & 18.34 & 420.88 & 124.01 & **1.95** & **1.95** \\ Lymph & 27.31 & 250.93 & 231.75 & **2.86** & **2.86** \\ Mushroom & 28.89 & 29.12 & 24.52 & **24.27** & 24.31 \\ Post-oper. & 15.17 & 8.78 & 16.38 & **2.03** & 2.61 \\ Tic-ac-toc & 31.76 & 62.23 & 12.76 & **4.73** & 8.44 \\ Vote & 23.57 & 2.65 & 3.13 & **3.09** & 3.15 \\ Wine & 20.87 & 175.36 & 59.52 & **1.82** & **1.82** \\ Zoo & 21.41 & 34.33 & 31.93 & 2.20 & **2.13** \\ \hline Average & 27.23 & 77.76 & 41.18 & **4.61** & 5.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The classifier building memory consumption in megabytes. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Data Sets** & **CBA** & **CMAR** & **FACA** & **ECARG** & **ECARG2** \\ \hline Anneal & 1.050 & **0.098** & 0.877 & 0.123 & 0.164 \\ Breast & 0.670 & 0.169 & 0.185 & **0.007** & 0.027 \\ Cars & 0.220 & 0.249 & 0.640 & **0.057** & 0.062 \\ Contact & 0.010 & 0.075 & 0.004 & **0.001** & 0.002 \\ Diabetes & 1.160 & 0.107 & 0.558 & **0.032** & 0.085 \\ Iris & 0.030 & 0.008 & 0.010 & **0.004** & **0.004** \\ Labor & 1.170 & 0.924 & 0.027 & **0.005** & **0.005** \\ Lymph & 1.320 & 3.782 & 3.700 & **0.016** & **0.016** \\ Mushroom & 25.830 & **0.104** & 21.500 & 4.049 & 4.128 \\ Post-oper. & 0.090 & 0.041 & 0.063 & **0.008** & 0.012 \\ Tic-ac-toc & 0.230 & 0.235 & 0.800 & **0.101** & 0.135 \\ Vote & 1.540 & 2.601 & 5.300 & **0.034** & 0.034 \\ Wined & 0.120 & 0.273 & 0.190 & **0.007** & **0.007** \\ Zoo & 0.900 & 0.005 & 0.047 & 0.020 & **0.013** \\ \hline Average & 2.453 & 0.623 & 2.422 & **0.319** & 0.335 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The classifier building time in seconds. Table 15 shows standard deviations of accuracy rate, the number of generated rules, building times, memory consumption, and F-measure of ECARG. The standard deviation values of building time and memory consumption are low and show that the building time and memory consumption in each fold is approximately marginal. The standard deviation values of the number of generated rules are relevant. The standard deviation values of accuracy rates and F-measure show that the values of accuracy rates and F-measure in each fold are marginally different on almost all datasets. However, when evaluating the small datasets, Contact-lenses, Labor, Lymph, and Post-operative, the standard deviation values are high because 10-fold cross-validation splits a very small testing set that can potentially affect the efficiency of the classifier. For example, the Contact-lenses dataset composes only 2 or 3 transactions in each testing set. Consequently, only one false classification occurs in the testing set and then reduces the accuracy rate dramatically. From the experimental results, the ECARG algorithm outperforms CBA, CMAR, and FACA in terms of accuracy rate and the number of generated rules. A key achievement of the ECARG algorithm is that the technique generates valid rules with 100% confidence to build classifiers. The high confidence demonstrates the high possibility of class occurrences occurring in an itemset. Therefore, the ECARG algorithm produces a small classifier but gives high accuracy. While the CBA, CMAR, and FACA algorithms build classifiers from CARs that meet the minimum confidence threshold, some of the CARs have low confidences so they may predict incorrect classese and then the accuracies of CBA, CMAR, and FACA are lower than the proposed algorithm in the most dataset. Moreover, ECARG outperforms the others in terms of building time and memory consumption.