Abstract
Interpretability and accuracy are two important features of fuzzy systems which are conflicting in their nature. One can be improved at the cost of the other and this situation is identified as “Interpretability-Accuracy Trade-Off”. To deal with this trade-off Multi-Objective Evolutionary Algorithms (MOEA) are frequently applied in the design of fuzzy systems. Several novel MOEA have been proposed and invented for this purpose, more specifically, Non-Dominated Sorting Genetic Algorithms (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Fuzzy Genetics-Based Machine Learning (FGBML), (2 + 2) Pareto Archived Evolutionary Strategy ((2 + 2) PAES), (2 + 2) Memetic- Pareto Archived Evolutionary Strategy ((2 + 2) M-PAES), etc. This paper introduces and reviews the approaches to the issue of developing fuzzy systems using Evolutionary Multi-Objective Optimization (EMO) algorithms considering ‘Interpretability-Accuracy Trade-off’ and mainly focusing on the work in the last decade. Different research issues and challenges are also discussed.
Highlights
Interpretability [1,2,3] and accuracy [4] are the two important features of a fuzzy system developed for a specific application
A Mamdani fuzzy rule-based system with different good trade-offs between complexity and accuracy has been developed by using multi-objective evolutionary algorithm in [63]
The accuracy is measured by correctly classified training patterns and the complexity is measured by the number of fuzzy rules and/or total number of antecedent conditions of fuzzy rules
Summary
Interpretability [1,2,3] and accuracy [4] are the two important features of a fuzzy system developed for a specific application. The identification of fuzzy systems from data samples for specific functions associates different tasks, like input selection, rule selection, rule generation, fuzzy partition, membership function tuning etc. These tasks can be implemented as an optimization or search process using Evolutionary. To deal with Interpretability-Accuracy Trade-Off in Fuzzy Systems, Multi-Objective Evolutionary. A taxonomy on existing proposals in EMOFS has been carried out, focusing mainly on Interpretability-Accuracy Trade-Off, multi-objective control problems and fuzzy association rule mining.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
