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.

Laboratory Risk Assessment Based on SHELL-HACCP-Cloud Model

“…Moreover, the aging failure of the experimental equipment has a great impact on the normal operation of the equipment, because ensuring the fluency of the experiment is also the key to preventing accidents. The results are consistent with those of Li Xiangong [42], who analyzed the laboratory accidents by correlation rules, affirming the validity of the model. Therefore, we believe that in the daily management process of the laboratory, there should be a strong emphasis on managing hazardous chemicals and equipment to prevent compromising the laboratory's safety.…”

“…FOR PEER REVIEW 14 of 18 [42], who analyzed the laboratory accidents by correlation rules, affirming the validity of the model. Therefore, we believe that in the daily management process of the laboratory, there should be a strong emphasis on managing hazardous chemicals and equipment to prevent compromising the laboratory's safety.…”