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Intuitionistic Fuzzy C-Least Squares Support Vector Regression with Sammon Mapping Clustering Algorithm

Author

Listed:
  • Kuo-PingLin

    (Lunghwa University of Science and Technology, Taiwan)

  • Teresa L. Ju

    (Lunghwa University of Science and Technology, Taiwan)

  • Ping-Feng Pai

    (Lunghwa University of Science and Technology, Taiwan)

  • Chih-Hung Kuo

    (Lunghwa University of Science and Technology, Taiwan)

Abstract

This study proposes a novel Intuitionistic fuzzy c-least squares support vector regression (IFCLSSVR) with sammon mapping clustering algorithm. The proposed clustering algorithm can obtain the advantages of intuitionistic fuzzy sets, LSSVR, and sammon mapping in actual clustering problems. Moreover, IFC-LSSVR with sammon mapping adopts particle swarm optimization (PSO) to search optimal parameters. Experiments on web-based adaptive learning environments data set, which is to provide enough or suitable knowledge for students/users, show that the proposed IFC-LSSVR with sammon mapping is more efficient than conventional algorithms such as the k-means (KM) and fuzzy c-means (FCM) clustering algorithm, in standard measurement indexes.

Suggested Citation

  • Kuo-PingLin & Teresa L. Ju & Ping-Feng Pai & Chih-Hung Kuo, 2015. "Intuitionistic Fuzzy C-Least Squares Support Vector Regression with Sammon Mapping Clustering Algorithm," Managing Intellectual Capital and Innovation for Sustainable and Inclusive Society: Managing Intellectual Capital and Innovation; Proceedings of the MakeLearn and TIIM Joint International Conference 2,, ToKnowPress.
    • Handle: RePEc:tkp:mklp15:129-132
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