Multiobjective genetic fuzzy rule selection of single granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions

Abstract

Multiobjective genetic fuzzy rule selection is based on the generation of a set of candidate fuzzy 
classification rules using a preestablished granularity or multiple fuzzy partitions with different 
granularities for each attribute. Then, a multiobjective evolutionary algorithm is applied to perform 
fuzzy rule selection. Since using multiple granularities for the same attribute has been sometimes 
pointed out as to involve a potential interpretability loss, a mechanism to specify appropriate single 
granularities at the rule extraction stage has been proposed to avoid it but maintaining or even 
improving the classification performance. In this work, we perform a statistical study on this proposal 
and we extend it by combining the single granularity-based approach with a lateral tuning of the membership 
functions, i.e., complete contexts learning. In this way, we analyze in depth the importance of determining 
the appropriate contexts for learning fuzzy classifiers. To this end, we will compare the single granularity-based 
approach with the use of multiple granularities with and without tuning. The results show that the performance 
of the obtained classifiers can be even improved by obtaining the appropriate variable contexts, i.e., appropriate granularities and membership function parameters.