# C-MWCAR: Classification Based on Multiple Weighted Class Association Rules
## 6 Discussion  
This paper proposed a multiple weighted class association rules-based classifier (i.e., C-MWCAR) and applied it to a real-world classification task. The experimental results demonstrated the effectiveness and interpretability of the proposed method. In this section, the advantages and limitations of C-MWCAR are discussed.  
The advantages of C-MWCAR are as follows. Firstly, the effectiveness of C-MWCAR is excellent. In particular, the BCSA of C-MWCAR can select representative CARs from each branch for classification, and the MWCC of C-MWCAR can not only solve the problem of having no matching CARs and the problem of having conflicting CARs but can also make full use of the power of multiple CARs that are the most similar to the subject to be classified. Therefore, C-MWCAR is much more accurate and useful when applied to real-world problems. Secondly, the interpretability of C-MWCAR is impressive. C-MWCAR can generate a set of CARs that can be used to explain why the subject is classified as hypertensive or normotensive to a certain degree. However, traditional classifiers, taking the Decision Tree classifier as an example, can generate a set of IF-THEN rules. Nevertheless, its interpretability is limited, since Decision Tree only takes one feature into consideration when building each tree node, instead of considering all the extracted features simultaneously, which may leave out some valuable association relationship among features. Moreover, the IF-THEN rules heavily rely on continuous feature values rather than discrete feature values, therefore, once the dataset changes slightly, the IF-THEN rules may have to adjust in order to adapt to the changed feature values. However, C-MWCAR deems the features non-independent intrinsically, which accords with the actual situation, so the interpretability of the CARs is more credible.  
The limitations of C-MWCAR are as follows. Firstly, the process of feature discretization may be affected by outliers. If there are some outliers for a feature, the feature values of hypertensive subjects or normotensive subjects in terms of the feature may be discretized into the same sub-range rather than different sub-ranges, which makes the feature lose its distinguishing ability. Secondly, the scale of the dataset collected in this study is not large since human-related physiological signals are hard to collect due to various restrictions in practice, which may make the classification performance not stable. To obtain more stable and reliable performance evaluation results, it is suggested to recruit more subjects to collect enough data samples. Thirdly, the equal-width binning strategy used in this study to discretize the features may be not the most powerful. In future, the performance of different feature discretization strategies should be investigated. Fourthly, only the most frequently used features are extracted in this study. To achieve higher classification accuracy, more features should be explored and studied. It is notable that the increased number of features almost only increases the time overhead of the process of CAR extraction but virtually has no effect on the time overhead of applying the classifier to classify the subjects.