# C-MWCAR: Classification Based on Multiple Weighted Class Association Rules
## 7 Conclusions and Future Work  
In this paper, we proposed a novel classification framework named C-MWCAR, which is able to obtain better classification performance by utilizing multiple weighted CARs. In particular, C-MWCAR can select representative and concise CARs based on the distribution, coverage, and redundancy of the CARs, and then build an effective classifier by considering the similarities between CARs and the instance and the weighted classification abilities of the CARs simultaneously. In addition, the designed CAR mining algorithm (i.e., DOCMA) can significantly reduce the time overhead used for extracting the complete set of CARs, which makes the application of CARs-based classifiers to real-world problems possible. Experimental results based on a real BCG dataset of 128 subjects demonstrated that the proposed C-MWCAR achieved 93.3%, 93.8%, and 92.7% in terms of ACC, SEN, and SPE, respectively, which outperformed four baselines significantly. In addition, the proposed DOCMA proved to be capable of reducing the time overhead by up to 81.2% when _sup_ and _conf_ were set to 0.25 and 0.8, respectively. Moreover, the visualization of the CARs denotedthat the mined CARs can explicitly reflect a subject's physiological status; in other words, the classification results were interpretable to some extent. The contributions of this paper can be summarized as follows. * To accelerate the extraction of CARs, a dictionary order-based CAR mining algorithm (DOCMA) was designed by optimizing the Apriori algorithm [26]. To be specific, we designed and proved a set of theorems and corollaries, by which the most time-consuming steps, i.e., the connection of frequent \(k-1\) itemSets and the pruning of infrequent k itemSets, can be greatly simplified. In addition, it can also pre-prune the set of frequent \(k-1\) itemSets, which further reduces the number of connection operations required. Experimental results showed that the proposed DOCMA saved up to 81.2% of time and outperformed three state-of-the-art CAR mining methods. * In order to deal with the numerous CARs, a branch-based CAR selection algorithm (BCSA) was designed to select the most representative and concise CARs. It first grouped all the CARs into non-overlapping branches, and then selected the most representative CARs from each of them, which made the selected CARs more representative. After that, we designed two metrics, named coverage and redundancy, by which the CARs contained little new information but much redundant information was filtered out, making the final set of selected CARs more concise and effective. * To obtain higher classification performance, we proposed a multiple weighted CARs-based classifier (MWCC) that classifies an instance by using multiple weighted CARs. Specifically, it first picks out a set of CARs that are most similar to the given instance, then it fuses their weighted classification abilities to compute the final classification result. This strategy can not only solve the problem of no matching CARs but can also achieve more accurate and robust classification results. * We applied the proposed C-MWCAR to a real classification task, i.e., hypertension diagnosis. Experimental results showed that C-MWCAR outperformed four state-of-the-art baselines, and achieved 93.3%, 93.8%, and 92.7% in terms of accuracy, sensitivity, and specificity, respectively. In addition, we found that the generated CARs can intuitively reflect the patient's physiological status, which signifies that C-MWCAR is interpretable to some extent. In the future, we will aim to build a new kind of CARs-based classifier that is much simpler but can produce better classification performance, and then test it on more real-world datasets. The work described in this article is the collaborative development of all authors. Conceptualization of data processing, G.L. and F.L. ; algorithm design, G.L. and F.L. ; data measurement, Y.Y., C.W. and Z.W. ; analysis, Y.Y., C.W. and Z.W. ; writing--original draft preparation, G.W. and Y.Z.