Knowledge Acquisition Tool for Learning Membership Function and Fuzzy Classification Rules from Numerical Data
Fadl MutaherBa-Alwi · International Journal of Computer Applications · 2013
Generating suitable membership function (MF) is the core step of fuzzy classification system.This paper presents a novel learning algorithm that generates automatically reasonable MFs for quantitative attributes.In addition, a set of an appropriate fuzzy classification rules (FCRs) are discovered from a given numerical data.Each fuzzy rule (FR) is of the form IF-THEN rule.The antecedent IF-part and consequent THEN-part contain fuzzy sets.Since MFs are generated automatically, the proposed fuzzy learning algorithm can be viewed as a knowledge acquisition tool for classification problems.Experimental results on Iris dataset are presented to demonstrate the contribution of the proposed approach for generating MFs.