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

2023-07-12

Annual Conference on Genetic and Evolutionary Computation

AI Summary

Traditional Michigan-style Learning Fuzzy Classifier Systems (LFCS) suffer from limited generalization in continuous domains due to fixed, pre-specified rule representations that cannot adapt to unknown data characteristics. To address this, we propose an adaptive rule representation mechanism featuring evolvable “fuzzy indicators”—parameters that dynamically select between crisp (hyper-rectangular) and fuzzy (triangular) membership functions, enabling online, context-aware rule-shape adaptation. This approach transcends rigid structural assumptions by unifying fuzzy logic, genetic evolution, and supervised learning within a single cohesive framework. Empirical evaluation across multiple continuous-domain benchmark tasks demonstrates that our method achieves significantly higher classification accuracy than the conventional UCS, while exhibiting superior robustness and stability under uncertainty—including noise corruption and missing values.

Technology Category

• Machine Learning: Evolutionary Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Impact of rule representation on classification performance in LFCSs
• Conventional rule representations struggle with unknown data characteristics
• Need for self-adaptive rule representation to improve accuracy and robustness

Innovation

• Self-adaptive rule representation in Fuzzy-UCS
• Evolutionary optimization of fuzzy indicators
• Robust performance in noisy, uncertain environments