FuzzyFeatureRank. Bringing order into fuzzy classifiers through fuzzy expressions

Abstract

This work presents FuzzyFeatureRank, a new feature reduction method inspired on PageRank to reduce the dimensionality of the feature space in supervised classification problems. More precisely, as it relies on a weighted directed graph, it is ultimately inspired on TextRank, a PageRank based method that adds weights to the edges to express the strength of the connections between nodes. The method is based on dividing each original feature used to describe the data into a set of fuzzy predicates and then ranking all of them by their ability to differentiate among classes in the light of the training set. In order to do that, both the information gained by each predicate and their redundancy with other already selected predicates are taken into account. The fuzzy predicates with the best scores can then be used as a reduced input to construct fuzzy classifiers that consider only the preselected predicates to build the antecedents of the fuzzy rules. The novelty of the proposal relies on being an approach halfway between feature selection and feature extraction approaches, being able to improve the discrimination ability of the original features but preserving the interpretability of the new features in the sense that they are fuzzy expressions. The experimental results support the suitability of the proposal.