A Neural-Assisted Fuzzy-Classifier System

Abstract

Learning Fuzzy-Classifier Systems (LFCSs), which leverage evolutionary algorithms to opti-(breakpoint)mize multiple local classification fuzzy rules, have been extensively studied as a form of eXplainable AI due to their ability to provide linguistically interpretable decision-making processes. However, because LFCSs discretely partition the input space, a single fuzzy rule may lack the necessary granularity to ac-(breakpoint)curately approximate class boundaries within a given region. To address this limitation, we propose a Neural-Assisted LFCS (NFCS), which divides the entire input space into linguistically explainable and unexplainable regions. Each region is then assigned either a fuzzy rule or a neural rule, thereby achieving both model interpretability and enhanced performance. This approach mitigates the issue inherent in LFCSs, where users are compelled to rely on fuzzy rules with low reliability (i.e., low classification accu-(breakpoint)racy) for regions that cannot be adequately explained linguistically. Experimental results on real-world classification problems demonstrate that NFCS significantly outperforms Fuzzy-UCS, a well-known LFCS, in terms of test accuracy without overfitting, which is often a concern when incorporating neural rules.