# Efficient Rule Generation for Associative Classification
## 5 Experimental Setting and Result  
The experiments were implemented and tested on a system with the following environment: Intel Core i3-6100u 2.3 GHz processor with 8 GB DDR4 main memory, running Microsoft Windows 10 64-bit version. Our algorithm is compared with the well-known algorithms CBA, CMAR, and FACA. All algorithms were implemented in java. The implementing java version of the CBA algorithm using CR-tree is from WEKA [26]. The implementation of CMAR in JAVA is from [27]. Four algorithms are tested on 14 datasets from the UCI Machine Learning Repository. The characteristics of the datasets are shown in Table 9. Ten-fold cross-validation is used to divide testing instances and training instances based on previous works [12; 17; 23; 26; 27]. Accuracy rates, the number of CARs, classifier building times, and memory consumption are used to measure the performance of the four algorithms. To study the sensitivity of thresholds on the ECARG algorithm, we set different minimum support thresholds and different minimum confidence thresholds in the experiment. First, we set the minimum support thresholds from 1% to 4% and analyze different minimum confidence thresholds between 60%, 70%, 80%, and 90%. Figure 1 shows the accuracy rates of all datasets. The results show that, when the minimum support thresholds are increased, the accuracy rates are decreased. If the minimum confidence thresholds are increased, the accuracy rates are slightly down. The highest accuracy rates are given in most datasets, Diabetes, Iris, Labor, Lymph, Mushroom, Post-operative, Tic-tac-toe, Vote, Wine, and Zoo, when minimum support and minimum confidence are set to 2% and 60%, respectively. Therefore, the minimum support is set to 2%, and minimum confidence is set to 60% in the next experiments. \begin{table}
\begin{tabular}{c c} \hline \hline
**CAR ID** & **CAR** \\ \hline R1 & \(\langle\mathit{attr1},a_{1}\rangle\to A\) \\ \hline R2 & \(\langle(\mathit{attr1},a_{2}),(\mathit{attr3},c_{2})\rangle\to B\) \\ \hline R3 & \(\langle\mathit{attr2},b_{3}\rangle\to A\) \\ \hline Default Class & \(B\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: All CARs from ECARG2. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Data Sets** & **\# of Attributes** & **\# of Classes** & **Instances** \\ \hline Anneal & 38 & 6 & 798 \\ Breast & 11 & 2 & 699 \\ Cars & 6 & 4 & 1,728 \\ Contact-lenses & 4 & 3 & 24 \\ Diabetes & 7 & 2 & 768 \\ Iris & 4 & 3 & 150 \\ Labor & 17 & 2 & 57 \\ Lymph & 18 & 4 & 148 \\ Mushroom & 22 & 2 & 8214 \\ Post-operative & 9 & 4 & 90 \\ Tic-tac-toe & 9 & 2 & 958 \\ Vote & 16 & 2 & 435 \\ Wined & 13 & 3 & 178 \\ Zoo & 17 & 7 & 101 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Characteristics of the experiment datasets. Table 10 reports the accuracy rates of the CBA, CMAR, FACA, ECARG, and ECARG2 algorithms on the UCI datasets. The results show that both of our algorithms outperform the others on average. This gain resulting from the methodology found the most efficient rule in each iteration and eliminated redundant rules simultaneously. To be more precise, we further analyzed the win-lost-tie records. Based on Table 10, the win-lost-tie records of the ECARG2 algorithm against CBA, CMAR, FACA, and ECARG in terms of accuracy are 11-3-0, 11-3-0, and 9-4-1, 8-6-0, respectively. We can observe that ECARG gives an accuracy slightly less than ECARG2. However, the ECARG algorithm results in the highest accuracy in 6 of 14 datasets. Figure 1: Accuracy rates in various \(minsup\) and \(mincof\) on all datasets. Table 11 shows the average number of CARs generated from CBA, CMAR, FACA, ECARG, and ECARG2 algorithms. The result shows that the CMAR algorithm generates the highest number of rules, while the ECARG algorithm generates the lowest. In particular, the ECARG algorithm generates 8 CARs on average against 14 datasets whereas the CBA, CMAR, FACA, and ECARG2 algorithms derive 19, 240, 13, and 18 CARs on average, respectively.