C-MWCAR: Classification Based on Multiple Weighted Class Association Rules
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/14/8082 |
| _version_ | 1797590442292805632 |
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| author | Gui Li Fan Liu Cheng Wu Yuan Yao Guangxin Wu Zhu Wang Yanchun Zhang |
| author_facet | Gui Li Fan Liu Cheng Wu Yuan Yao Guangxin Wu Zhu Wang Yanchun Zhang |
| author_sort | Gui Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T01:20:33Z |
| format | Article |
| id | doaj.art-5698470b67614ef6bc492900b956c262 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:20:33Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5698470b67614ef6bc492900b956c2622023-11-18T18:07:55ZengMDPI AGApplied Sciences2076-34172023-07-011314808210.3390/app13148082C-MWCAR: Classification Based on Multiple Weighted Class Association RulesGui Li0Fan Liu1Cheng Wu2Yuan Yao3Guangxin Wu4Zhu Wang5Yanchun Zhang6Nanjing Research Institute of Electronics Technology, Nanjing 210039, ChinaNanjing Research Institute of Electronics Technology, Nanjing 210039, ChinaNanjing Research Institute of Electronics Technology, Nanjing 210039, ChinaNanjing Research Institute of Electronics Technology, Nanjing 210039, ChinaNanjing Research Institute of Electronics Technology, Nanjing 210039, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi’an 710072, ChinaCyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, ChinaClassification 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.https://www.mdpi.com/2076-3417/13/14/8082classificationclass association ruleinterpretable classifierhypertension diagnosis |
| spellingShingle | Gui Li Fan Liu Cheng Wu Yuan Yao Guangxin Wu Zhu Wang Yanchun Zhang C-MWCAR: Classification Based on Multiple Weighted Class Association Rules Applied Sciences classification class association rule interpretable classifier hypertension diagnosis |
| title | C-MWCAR: Classification Based on Multiple Weighted Class Association Rules |
| title_full | C-MWCAR: Classification Based on Multiple Weighted Class Association Rules |
| title_fullStr | C-MWCAR: Classification Based on Multiple Weighted Class Association Rules |
| title_full_unstemmed | C-MWCAR: Classification Based on Multiple Weighted Class Association Rules |
| title_short | C-MWCAR: Classification Based on Multiple Weighted Class Association Rules |
| title_sort | c mwcar classification based on multiple weighted class association rules |
| topic | classification class association rule interpretable classifier hypertension diagnosis |
| url | https://www.mdpi.com/2076-3417/13/14/8082 |
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