Fuzzy systems that automatically derive fuzzy if-then rules from numeric da
ta have been developed Most have to predefine membership functions in order
to learn. Hong and Lee proposed a general learning method that automatical
ly derives fuzzy if-then rules and membership functions from a set of given
training examples using a decision table. All available attributes were in
cluded in the decision table and the initial membership functions for each
attribute were built according to the predefined smallest unit. Although Ho
ng and Lee's method accurately derives the fuzzy if-then rules and final me
mbership functions, the decision table and the initial membership functions
are complex if there are many attributes or if the predefined unit is smal
l. We improve Hong and Lee's method by first selecting relevant attributes
and building appropriate initial membership functions. These attributes and
membership functions are then used in a decision table to derive final fuz
zy if-then rules and membership functions. Experimental results on Iris dat
a show that the proposed method effectively induces membership functions an
d fuzzy if-then rules. (C) 1999 Elsevier Science B.V. All rights reserved.