# C-MWCAR: Classification Based on Multiple Weighted Class Association Rules
## 5 Experimental Evaluation
### Evaluation Results  
#### 5.2.1 Classification Performance Analysis  
We first quantitatively study the performance improvements contributed by the proposed BCSA and MWCC. As shown in Table 6, we set four methods for comparison, named as \"NONE\", \"BCSA\", \"MWCC,\" and \"BCSA + MWCC\", respectively. In particular, \"NONE\" means that neither the BCSA nor the MWCC is adopted when building the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Type** & **Por.\({}^{*}\) 1** & **Pro. 2** & **Pro. 3** & **Por. 4** & **Por. 5** & **Por. 6** & **Por. 7** & **Por. 8** & **Por. 9** & **Por. 10** & **SUM** \\ \hline Hypertensive & 6 & 6 & 6 & 6 & 6 & 6 & 6 & 6 & 6 & 7 & 61 \\ Normotensive & 7 & 7 & 7 & 7 & 7 & 7 & 7 & 7 & 6 & 68 \\ Subtotal & 13 & 13 & 13 & 13 & 13 & 13 & 13 & 13 & 13 & 13 & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The division of the dataset.  
classifier, \"BCSA\" means that the BCSA algorithm is adopted while the MWCC algorithm is not adopted when building the classifier, and \"BCSA + MWCC\" indicates that both of the methods are adopted when building the classifier. From Table 6, we find that \"BCSA\" obtains 5.0% of ACC, 5.6% of SEN, and 5.4% of SPE improvements, while \"MWCC\" obtains 7.1% of ACC, 8.1% of SEN, and 6.3% of SPE improvements in comparison with \"NONE\", which indicates that selecting a representative and concise set of CARs and making use of multiple weighted CARs are very conducive to achieving more accurate results. Moreover, once BCSA and MWCC are combined, the performance significantly improves and ACC, SEN, and SPE increase to 93.3%, 93.8%, and 92.7%, respectively, which shows the effectiveness of our methods.  
Afterwards, we compared the proposed C-MWCAR with four baselines proposed by [PERSON] et al. [29], [PERSON] et al. [20], [PERSON] et al. [7], and [PERSON] et al. [8]. [PERSON] et al. [29] extracted many HRV features and then directly inputted them into an LibSVM classifier. [PERSON] et al. transformed the extracted HRV features into different feature representations by using feature pooling techniques, and then modeled obtained feature representations by utilizing a L1-regularized logistic regression classifier [17]. The third [7] and fourth [8] methods are two famous CARs-based classifiers, i.e., CBA and CMAR. CBA uses only one CAR to classify a given instance (if there are no matched CARs, the instance will be labeled with a default class), while CMAR employs multiple CARs to determine the final class labels. Note that for fair comparison, these baselines are corrected based on the BCG dataset collected in this study. In particular, Refs. [20; 29] extracted their own HRV features, while CBA and CMAR utilized the HRV features extracted in this study. In addition, the best parameter combinations of the baselines were adopted. As shown in Figure 4, C-MWCAR obtained the highest performance, and outperformed the method in [29] by 17.7%, 20.8%, and 17.9% in terms of ACC, SEN, and SPE, respectively, which indicates that extracting the associate relationship among features is useful for classification. The method in [20] showed slightly higher performance than the method in [29] (2.2% of ACC, 2.0% of SEN, and 3.5% of SPE) as it employed more complex feature combinations; however, its performance was much lower than C-MWCAR (13.3% of ACC, 13.8% of SEN, and 11.3% of SPE). The performance of CBA was lower than CMAR by 4.3%, 4.6%, and 4.3%, and much lower than C-MWCAR by 10.2%, 12.1%, and 9.9% in terms of ACC, SEN, and SPE, respectively, which means that utilizing multiple CARs can provide more useful information for classification. In particular, C-MWCAR and CMAR use the same features but the former outperformed the latter by 5.9% of ACC, 7.5% of SEN, and 5.6% of SPE, which may be because C-MWCAR can select the most similar CARs and exploit their weighted classification abilities when classifying each instance.