Intuitionistic fuzzy C-regression by using least squares support vector regression
•We developed a novel clustering algorithm for solving actual clustering problems.•The novel clustering algorithm improves conventional fuzzy c-regression model.•Empirical results indicate that the proposed clustering algorithm has superior performance. This paper proposes a novel intuitionistic fuz...
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| Published in: | Expert systems with applications Vol. 64; pp. 296 - 304 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.12.2016
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| Subjects: | |
| ISSN: | 0957-4174, 1873-6793 |
| Online Access: | Get full text |
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| Abstract | •We developed a novel clustering algorithm for solving actual clustering problems.•The novel clustering algorithm improves conventional fuzzy c-regression model.•Empirical results indicate that the proposed clustering algorithm has superior performance.
This paper proposes a novel intuitionistic fuzzy c-least squares support vector regression (IFC-LSSVR) with a Sammon mapping clustering algorithm. Sammon mapping effectively reduces the complexity of raw data, while intuitionistic fuzzy sets (IFSs) can effectively tune the membership of data points, and LSSVR improves the conventional fuzzy c-regression model. The proposed clustering algorithm combines the advantages of IFSs, LSSVR and Sammon mapping for solving actual clustering problems. Moreover, IFC-LSSVR with Sammon mapping adopts particle swarm optimization to obtain optimal parameters. Experiments conducted on a web-based adaptive learning environment and a dataset of wheat varieties demonstrate that the proposed algorithm is more efficient than conventional algorithms, such as the k-means (KM) and fuzzy c-means (FCM) clustering algorithms, in standard measurement indexes. This study thus demonstrates that the proposed model is a credible fuzzy clustering algorithm. The novel method contributes not only to the theoretical aspects of fuzzy clustering, but is also widely applicable in data mining, image systems, rule-based expert systems and prediction problems. |
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| AbstractList | •We developed a novel clustering algorithm for solving actual clustering problems.•The novel clustering algorithm improves conventional fuzzy c-regression model.•Empirical results indicate that the proposed clustering algorithm has superior performance.
This paper proposes a novel intuitionistic fuzzy c-least squares support vector regression (IFC-LSSVR) with a Sammon mapping clustering algorithm. Sammon mapping effectively reduces the complexity of raw data, while intuitionistic fuzzy sets (IFSs) can effectively tune the membership of data points, and LSSVR improves the conventional fuzzy c-regression model. The proposed clustering algorithm combines the advantages of IFSs, LSSVR and Sammon mapping for solving actual clustering problems. Moreover, IFC-LSSVR with Sammon mapping adopts particle swarm optimization to obtain optimal parameters. Experiments conducted on a web-based adaptive learning environment and a dataset of wheat varieties demonstrate that the proposed algorithm is more efficient than conventional algorithms, such as the k-means (KM) and fuzzy c-means (FCM) clustering algorithms, in standard measurement indexes. This study thus demonstrates that the proposed model is a credible fuzzy clustering algorithm. The novel method contributes not only to the theoretical aspects of fuzzy clustering, but is also widely applicable in data mining, image systems, rule-based expert systems and prediction problems. |
| Author | Chang, Hao-Feng Wang, Ching-Hsin Chen, Tung-Lian Lu, Yu-Ming Lin, Kuo-Ping |
| Author_xml | – sequence: 1 givenname: Kuo-Ping surname: Lin fullname: Lin, Kuo-Ping email: kplin@mail.lhu.edu.tw organization: Department of Information Management, Lunghwa University of Science and Technology, Taoyuan 33306, Taiwan, ROC – sequence: 2 givenname: Hao-Feng surname: Chang fullname: Chang, Hao-Feng email: nih3630@gmail.com organization: Chung-Hua University, 707, Sec. 2, WuFu Rd., Hsinchu 300, Taiwan, ROC – sequence: 3 givenname: Tung-Lian surname: Chen fullname: Chen, Tung-Lian email: tlchen@chu.edu.tw organization: Chung-Hua University, 707, Sec. 2, WuFu Rd., Hsinchu 300, Taiwan, ROC – sequence: 4 givenname: Yu-Ming surname: Lu fullname: Lu, Yu-Ming email: yuminglu@ms2.hinet.net organization: Department of Information Management, Lunghwa University of Science and Technology, Taoyuan 33306, Taiwan, ROC – sequence: 5 givenname: Ching-Hsin surname: Wang fullname: Wang, Ching-Hsin email: thomas_6701@yahoo.com.tw organization: Institute of Project Management, National Chin-Yi University of Technology, Taiwan, ROC |
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