# EEFR-R: extracting effective fuzzy rules for regression problems, through the cooperation of association rule mining concepts and evolutionary algorithms

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## Abstract

Fuzzy rule-based systems, due to their simplicity and comprehensibility, are widely used to solve regression problems. Fuzzy rules can be generated by learning from data examples. However, this strategy may result in high numbers of rules that most of them are redundant and/or weak, and they affect the systems’ interpretability. Hence, in this paper, a new rule learning method, EEFR-R, is proposed to extract the effective fuzzy rules from regression data samples. This method is formed through the cooperation of association rule mining concepts and evolutionary algorithms in the three stages. Indeed, the components of a Mamdani fuzzy rule-based system are generated during the first two stages, and then, they will be refined through some modifications in the last stage. In EEFR-R, fuzzy rules are extracted from numerical data using the idea of Wang and Mendel’s method and utilizing the concepts of Support and Confidence; furthermore, a new rule pruning method is presented to refine these rules. By employing this method, non-effective rules can be pruned in three different modes as the preferences of a decision maker. The proposed model and its stages were validated using 19 real-world regression datasets. The experimental results and the conducted statistical tests confirmed the effectiveness of EEFR-R in terms of complexity and accuracy and in comparison with the three state-of-the-art regression solutions.

## Keywords

Discretization Fuzzy rule learning Rule pruning Support and confidence Particle swarm optimization## Notes

### Acknowledgements

This study was not funded at all.

### Compliance with ethical standards

### Conflict of interest

The authors declare that they have no conflict of interest.

### Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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