# C-MWCAR: Classification Based on Multiple Weighted Class Association Rules
## 2 Background  
This section will briefly introduce the background and the literature relevant to hypertension diagnosis and classifier construction. Hypertension diagnosis is an important task in the real world. Many studies diagnose hypertension by extracting features from an ECG signal and directly inputting them into a common machine learning algorithm, such as SVM, logistic regression, Decision Tree, and so on. For example, [PERSON] et al. [19] extracted many HRV-related features from RR intervals sequence based on ECG signal and then fed them into a SVM to identify hypertenusive subjects. Similarly, [PERSON] et al. [20] employed a logistic regression algorithm to discriminate hypertensive and normotensive. The performance of these methods are good; however, the outputted classification results are unable to interpret why the subjects suffer from hypertension, which is unfavorable for doctors to analyze the patient's condition in depth. To solve this issue, [PERSON] et al. [21] employed a decision tree-based classifier to model the HRV-related features and obtained a set of dendroid decision rules. If a subject matches a rule antecedent, he or she will be classified by the corresponding rule consequent, i.e., the class label. These rules can be used to explain why subjects are hypertensive to some extent. However, the method can only consider just one feature at each tree node when constructing the decision tree, which may lose the useful correlation relationship among features and hence lead to limited classification performance. Hence, it is important to design a novel kind of classifier that can not only diagnose hypertension accurately but can also generate informative classification results to interpret or reflect the subject's physiological state. The CARs-based classifier is viewed as a very promising technique, and has been successfully applied in many fields, such as identifying heart disease diagnosis [16; 17], activity recognition [9], predicting pandemic influenza [14], learning business rules [11], and discriminating protein-protein interaction type [15], etc. The CARs-based classifier considers all the features at the same time and extracts meaningful association rules from them, which significantly prompts the classification performance. Additionally, it can also generate a set of informative CARs that can accurately reflect the subject's physiological state. The first CARs-based classifier is called CBA (classification based on associations) [7] and its construction process is comprised of three steps: mining CARs, selecting a powerful subset of CARs, and building a classifier based on the selected CARs. After that, many follow-up studies were proposed [10; 12; 8; 13], but their main steps remain unchanged. In particular, there are some issues with each step when applying CARs-based classifiers in real-world problems. In the first step, the mining of CARs is usually time-consuming. For example, CBA [7] extracts CARs based on the Apriori algorithm, which needs to iterate the dataset many times and hence results in a huge time overhead. To solve this issue, many new methods have been proposed, including methods based on FP-growth and the ER-tree [8], vertical dataset layout [22], equivalence class rule tree [23], ECR-tree with Obfoster [12], lattice [24], and multi-core processor architecture [18]. In the second step, how to select an effective subset of CARs is a real challenge. Most methods select CARs by considering the coverage of the dataset [7; 8; 9; 11; 14; 15; 16; 17]. For example, [PERSON] et al. [7] selects CARs that can accurately match at least one instance. Similarly, [PERSON] et al. [8] designs a coverage threshold \(\delta\) to make sure each instance in the training set can be covered by \(\delta\) rules, aiming to increase the number of selected CARs. [PERSON] et al. [15] takes both the significance of CARs and overlapping rules into consideration. However, these CARs' selection methods ignore the redundancy and distribution of the CARs.