Fuzzy-UCS Revisited: Self-Adaptation of Rule Representations in Michigan-Style Learning Fuzzy-Classifier Systems

TL;DR

This paper introduces Adaptive-UCS, a self-adaptive rule representation mechanism for Michigan-style Learning Fuzzy-Classifier Systems, which optimizes rule shapes to improve classification accuracy and robustness in uncertain and noisy environments.

Contribution

It proposes a novel fuzzy indicator parameter and evolutionary optimization for adaptive rule representation in LFCSs, enhancing performance over traditional methods.

Findings

Adaptive-UCS outperforms conventional UCSs in classification accuracy.

It demonstrates robustness with noisy and uncertain data.

The system effectively adapts rule shapes for improved performance.

Abstract

This paper focuses on the impact of rule representation in Michigan-style Learning Fuzzy-Classifier Systems (LFCSs) on its classification performance. A well-representation of the rules in an LFCS is crucial for improving its performance. However, conventional rule representations frequently need help addressing problems with unknown data characteristics. To address this issue, this paper proposes a supervised LFCS (i.e., Fuzzy-UCS) with a self-adaptive rule representation mechanism, entitled Adaptive-UCS. Adaptive-UCS incorporates a fuzzy indicator as a new rule parameter that sets the membership function of a rule as either rectangular (i.e., crisp) or triangular (i.e., fuzzy) shapes. The fuzzy indicator is optimized with evolutionary operators, allowing the system to search for an optimal rule representation. Results from extensive experiments conducted on continuous space problems demonstrate that Adaptive-UCS outperforms other UCSs with conventional crisp-hyperrectangular and fuzzy-hypertrapezoidal rule representations in classification accuracy. Additionally, Adaptive-UCS exhibits robustness in the case of noisy inputs and real-world problems with inherent uncertainty, such as missing values, leading to stable classification performance.