Improving a fuzzy association rule-based classification model by granularity learning based on heuristic measures over multiple granularities

Abstract

A multi-objective evolutionary fuzzy rule selection process extracts a subset of fuzzy rules from an initial set, by applying a multi-objective evolutionary algorithm. Two approaches can be used to determine the number of terms (i.e. the granularity) associated with the linguistic variables that appear in the rules: a pre-established single granularity can be chosen, or a multiple granularities approach can be preferred. The latter favors a reduction in the number of extracted rules, but it also brings to a possible loss of interpretability. To prevent this problem, suitable granularities can be determined by applying automatic techniques before the initial rule generation process. In this contribution, we investigate how the application of a single granularity learning approach influences the performance of fuzzy associative rule-based classifiers. The aim is to reduce the complexity of the obtained models, trying to maintain a good classification ability.