Effect of merging order on performance of fuzzy induction^☆

Article type: Research Article

Authors: Hong, Tzung-Pei^{a; *} | Lee, Chai-Ying^{b; 1}

Affiliations: [a] Department of Information Management. I-Shou University, Kaohsiung, 84008, Taiwan, ROC | [b] Industrial Technology Research Institute, 195-3 Chung Hsing Road, Section 4 Chutung, Hsinchu, Taiwan, ROC

Correspondence: [*] Corresponding author. E-mail: [email protected]

Note: [☆] This research was supported by the National Science Council of the Republic of China under contract NSC87-2213-E-214-003.

Note: [1] E-mail: [email protected]

Abstract: Most fuzzy controllers must predefine membership functions and fuzzy inference rules to map numeric data into fuzzy linguistic values and make fuzzy reasoning work. In T.P. Hong, C.Y. Lee, Fuzzy Sets and Systems 84 (1996) 33–47, we proposed a general learning method for automatically deriving fuzzy-if-then rules and membership functions from a set of given training examples by merging the decision tables and membership functions. The merging order of the attributes, however, has great consequences on the accuracy of the final learning results. In this paper, we present appropriate heuristics to determine the merging order. Less relevant attributes will be processed earlier to reduce the complexity of the decision table. Experiments were also made, showing that our proposed heuristics demonstrate good performance.

Keywords: Fuzzy controller, Fuzzy learning, Decision table, Membership function, Merging order

DOI: 10.3233/IDA-1999-3205

Journal: Intelligent Data Analysis, vol. 3, no. 2, pp. 139-151, 1999