A Variable-Length Fuzzy Set Representation for Learning Fuzzy-Classifier Systems

This paper introduces a novel Learning Fuzzy-Classifier System (LFCS) that incorporates variable-length fuzzy sets in rule antecedents to enhance classification accuracy and mitigate overfitting in real-world data scenarios. Traditional LFCSs utilize fixed-length fuzzy sets, which can limit their performance, especially when the rule set size is restricted in high-dimensional input space. The proposed algorithm, Fuzzy-UCSv (i.e., the Fuzzy-UCS classifier system with a variable-length fuzzy set representation), addresses these limitations by allowing the number of fuzzy sets per dimension in rule-antecedents to vary. Fuzzy-UCSv aims to tackle two primary challenges identified in LFCS: the unnecessary optimization of membership functions for irrelevant features and the difficulty in forming optimal classification boundaries with a single membership function per feature. By optimizing the number of membership functions for each rule using an evolutionary algorithm, Fuzzy-UCSv acquires rules that ignore non-contributing features and effectively cover complex input spaces, significantly improving test accuracy without increasing the risk of overfitting. Experimental results demonstrate that Fuzzy-UCSv outperforms conventional Fuzzy-UCS and other machine learning techniques in terms of test accuracy.