Building a concise decision table for fuzzy rule induction

Abstract

Fuzzy systems that can automatically derive fuzzy if then rules and membership functions from numeric data have been developed previously. In this paper, we propose two new fuzzy learning methods for automatically deriving membership functions and fuzzy if-then rules from a set of given training examples. The proposed methods first select relevant attributes and build appropriate initial membership functions. They then simplify the intervals and the membership functions of each attribute before the decision table is formed. These attributes and membership functions are then used in a decision table to derive the final fuzzy if-then rules and membership functions. Experimental results on Iris data show that our methods can achieve a high accuracy. The proposed methods are thus useful in constructing membership functions and in managing uncertainty and vagueness. They can also reduce the time and effort needed to develop a fuzzy knowledge base.