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

arXiv: https://arxiv.org/abs/2505.06017 · HTML: https://mlsanity.com/paper/2505.06017/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 56 references that are in the map.
- First posted on arXiv 9 May 2025.

## Builds on (most cited first, 5 of 5)

- [LCS-DIVE: An Automated Rule-based Machine Learning Visualization Pipeline for Characterizing Complex Associations in Classification](https://mlsanity.com/paper/2104.12844/) (2021)
- [Optimality-based Analysis of XCSF Compaction in Discrete Reinforcement Learning](https://mlsanity.com/paper/2009.01476/) (2020)
- [A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning Policies](https://mlsanity.com/paper/2305.09922/) (2023)
- [Knowledge Representation in Learning Classifier Systems: A Review](https://mlsanity.com/paper/1506.04002/) (2015)
- [Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System](https://mlsanity.com/paper/2207.05582/) (2022)

## Built on by (most cited first, 2 of 2)

- [Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems](https://mlsanity.com/paper/2506.03602/) (2025)
- [A Class Inference Scheme With Dempster-Shafer Theory for Learning Fuzzy-Classifier Systems](https://mlsanity.com/paper/2506.03588/) (2025)

## Abstract (arXiv)

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.