GPR: A Python implementation of an extremely simple classifier based on fuzzy logic and gene expression programming

In this work, we present a Python-based implementation of an extremely simple classifier (GPR), which combines gene expression programming (GEP) features and the algebraic representation of the 'if-then' fuzzy rules theory of the Takagi-Sugeno fuzzy inference system. Generated fuzzy metarules are highly interpretable and suitable for many applications. We provide an open-source Python implementation of the GPR algorithm to enable the use of the algorithm without any commercial software tools and open access to the research community. We also added enhancements to improve the readability and interpretability of the rules.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

In this work, a Python-based implementation of a simple classifier called GPR is presented, which combines gene expression programming (GEP) features and the algebraic representation of the 'if-then' fuzzy rules theory of the Takagi-Sugeno fuzzy inference system. The generated fuzzy metarules are highly interpretable and suitable for a wide range of applications. The open-source Python implementation of the GPR algorithm allows for its use without any commercial software tools and provides open access to the research community. Enhancements have been made to improve the readability and interpretability of the rules.