Effect of merging order on performance of fuzzy induction
Tzung‐Pei Hong · Intelligent Data Analysis · 1999
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.