International Journal of Applied Science and Engineering

Published by Chaoyang University of Technology

Huiqin Jiangab, Rung-Ching Chenb*, Qiao-En Liub and Su-Wen Huangb

aDepartment of Information Engineering, Xiamen University of Technology Xiamen, China
bDepartment of Information Management, Chaoyang University of Technology Taichung, Taiwan (R.O.C.)


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With high-dimensional data appearing, the number of fuzzy rules increases which degrade the interpretability and increases the computation complexity of the fuzzy rule-based system. In this paper, we proposed a rule-reduced algorithm. Through the sparse encoding of the fuzzy basis functions (FBFs), rules are reduced. Least angle regression algorithm is proposed here to select the important rules. Compared with other sparse encoding algorithm, Least angle regression algorithm has the advantage of lower computation complexity and better performance. The experimental results show that our proposed algorithm has excellent performance, especially for high-dimension data.

Keywords: Data-driven FISs; lasso; LARS; FCM; sparse enconding.

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Received: 2019-07-30
Revised: 2019-09-11
Accepted: 2019-10-21
Publication Date: 2019-11-01

Cite this article:

Jiang, H., Chen, R.C., Liu, Q.E., Huang, S.W. 2019. Fuzzy rules reduction based on sparse coding. International Journal of Applied Science and Engineering, 16, 215-227.

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