A Method of Designing Interpretable Genetic Fuzzy Classification System Based On Mutating Parameters

Hong JI, Ming Ma
2015 Proceedings of the 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering   unpublished
This paper discusses the application of generating fuzzy rules with word computing in genetic fuzzy classification system, and proposes a new method to design genetic fuzzy classification system. The new algorithm generates initial fuzzy rules population with expertise of the randomly selecting samples, and adds mutating parameters to adjust the shape of membership function of fuzzy partition in order to expand the algorithm's search space. Experiments show that the new algorithm has better classification accuracy with shorter length of rules.
doi:10.2991/isrme-15.2015.46 fatcat:jofclgale5dj7lz2hxtynrxr5u