TY - JOUR
T1 - C-MWCAR
T2 - Classification Based on Multiple Weighted Class Association Rules
AU - Li, Gui
AU - Liu, Fan
AU - Wu, Cheng
AU - Yao, Yuan
AU - Wu, Guangxin
AU - Wang, Zhu
AU - Zhang, Yanchun
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/7
Y1 - 2023/7
N2 - 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.
AB - 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.
KW - class association rule
KW - classification
KW - hypertension diagnosis
KW - interpretable classifier
UR - http://www.scopus.com/inward/record.url?scp=85166248257&partnerID=8YFLogxK
U2 - 10.3390/app13148082
DO - 10.3390/app13148082
M3 - 文章
AN - SCOPUS:85166248257
SN - 2076-3417
VL - 13
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 14
M1 - 8082
ER -