# C-MWCAR: Classification Based on Multiple Weighted Class Association Rules
###### Abstract  
Classification is a very important task in data mining and pattern analysis, which have been widely used to solve various real-world problems. To obtain better classification performance, in this paper, we propose a novel classification framework based on multiple weighted class association rules (C-MWCAR), whose key idea is to transform the association among features into a set of class association rules (CARs), then classify unknown instances based on the CARs obtained. Concretely, C-MWCAR consists of a dictionary order-based CAR mining algorithm (DOCMA), a branch-based CAR selection algorithm (BCSA), and a multiple weighted CARs-based classifier (MWCC). Specifically, DOCMA mines the complete set of CARs, from which BCSA further selects a representative and concise set of CARs based on the distribution, coverage, and redundancy of the mined CARs. When classifying an unknown instance, MWCC picks out a set of CARs that are most similar to the given instance and computes the weighted importance of those CARs. Finally, the class label of the given instance will be determined by the similarities between the instance and the CARs and the weighted importance of the CARs. Furthermore, we apply the proposed C-MWCAR to a real-world classification task, i.e., hypertension diagnosis, based on a real dataset of 128 subjects. Experimental results indicate that C-MWCAR outperforms four baseline methods and achieves 93.3%, 93.8%, and 92.7% in terms of accuracy, sensitivity, and specificity, respectively. classification; class association rule; interpretable classifier; hypertension diagnosis +
Footnote †: journal: Medical  
To cope with the above problems, a new type of classifiers, called associative classifiers, have been recently proposed, and they include CBA [7], CMAR [8], and so on. Their core idea is to exploit the association relationship among features for classification. In particular, the main steps of the simplest associative classifier are as follows. It first transforms the association relationship among features into a set of class association rules (CARs). A CAR is made up of two parts, i.e., antecedent and consequent. Specifically, the antecedent is comprised of discretized features, while the consequent is a class attribute. Afterwards, it selects a subset of CARs and a default class to build the final classifier. When classifying an instance, it compares the instance with the selected CARs, one by one, and the instance will be classified as the consequent of the CAR that first matches the instance, or the default class if no matching CARs exists. In particular, it has been demonstrated that CARs-based classifiers are usually more accurate than traditional classifiers such as C4.5, Hidden Markov Model (HMM), Inductive Learning Algorithm (ILA), and Naive Bayes, both theoretically [9; 10] and experimentally [7; 11]. Moreover, benefiting from the obtained informative CARs, the classification results can be interpreted to some extent. However, it is not a trivial task when applying associative classifiers to real-world classification tasks for the following reasons. First, extracting a complete set of CARs is very time-consuming [8; 12; 13], especially when the number of features is high. Second, as the number of extracted CARs is high [7; 8; 9; 11; 14; 15; 16; 17], how to select the best subset of CARs to build the most robust and accurate classifier is a real challenge. Third, it is difficult to utilize just one CAR to accurately classify future instances [8]. Moreover, due to the complexity of real-world problems, it is likely that future instances may be unable to match any CARs. In other words, the future instances cannot be assigned a proper class label. To tackle the problems facing CARs-based classifiers, this study proposes a novel classification framework based on multiple weighted class association rules (C-MWCAR).