# C-MWCAR: Classification Based on Multiple Weighted Class Association Rules
## 5 Experimental Evaluation
### Evaluation Results
#### 5.2.3 Parameter Optimization  
The support and confidence are two key parameters when mining CARs, and directly affect the number of extracted CARs. If the number of CARs is too small, it may miss some useful CARs; however, if the number of CARs is too large, it will incur too much noise and redundancy. The number of mined CARs under different combinations of support and confidence are shown in Figure 6, which shows that the number of the CARs mainly depends on the support rather than the confidence. It soars sharply if the support decreases, while it increases slightly if the confidence decreases. In particular, the number of CARs is neither too small nor too large when the support is set to 0.25. In addition, we notice that the growth of the number of CARs caused by the decrease of confidence when support is set to 0.3 or 0.2 is more moderate than that when support is set to 0.25, which signifies that the distribution of the CARs is much more even with respect to the confidence when support equals 0.25. Therefore, the support and confidence are finally set to 0.25 and 0.75, respectively. When selecting CARs, the length of the same prefix \(k\), the redundancy threshold \(R\), and the coverage threshold \(C\) directly determine the final CARs set, which will further affect the final classification results. Too large an \(L\) value may lead to missing some effective CARs, but too small an \(L\) value may make the selected CARs almost unrepresentative. Likewise, a big \(R\) value makes selected CARs contain too much redundant information, while a small \(R\) will leave out useful information. Moreover, if the value of \(C\) is too large or too small, it may lead to over-fitting or under-fitting. In addition, in the classification process, the number of similar CARs used to classify instances, i.e., \(K\), is also a key parameter that can directly affect the classification performance. Obviously, too small an \(N\) value cannot obtain enough information from the CARs for classification, while too large an \(N\) value will introduce unimportant information and hence distort the classification results. As shown in Figure 7, we investigate the performance as \(k\), \(R\), \(C\), and \(K\) vary independently. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Algorithms**} & \multirow{2}{*}{**Strategies Used to Mine CARs**} & \multicolumn{3}{c}{**Time Overhead 1**} \\ \cline{3-5}  & & **Sup = 0.2** & **Sup = 0.25** & **Sup = 0.3** \\ \hline DOCMA & Use the Apriori-like algorithm with optimized strategies to generate rules & 3976.3 s & 1369.7 s & 129.9 s \\ CAR-Miner [12] & Mine CARs based on the modified ECR-tree with & 4123.5 s & 2156.8 s & 421.8 s \\ ECR-CARM [22] & Use equivalence class rule tree (ECR-tree) to generate candidate rules & 6425.8 s & 3482.5 s & 761.3 s \\ PMCAR [24] & Mine CARs with parallel and distributed approaches & 3379.4 s & 1624.4 s & 249.5 s \\ \hline \end{tabular}  
* \({}^{1}\) The confidence is fixed at 0.8.  
\end{table} 
Table 7: Comparison of the efficiency of mining CARs between DOCMA and three state-of-the-art baselines. Figure 6: The number of mined CARs under different combinations of support and confidence. Figure 7: Optimization of the key parameters of the C-MWCAR. (**a–d**) reflect the variation of ACC, SEN, and SPE when the length of the branch prefix, the redundancy threshold, the coverage threshold, and the number of similar CARs change, respectively. Note that when studying the effects of a specific parameter, the other parameters are set to default values. The default values of these four parameters are 4, 0.6, 0.85, and 3, respectively. Figure 7a shows that ACC, SEN, and SPE have the same trend and they all first increase and then decrease. They obtain their maximum when the length of the same prefix is set to 4, 3, and 5, respectively. In order to keep all three metrics high, we make a trade-off and suggest setting the length of the prefix as 4. As shown in Figure 7b, the maximum values of the three metrics are obtained when the redundancy threshold is 0.6.