A Feature Ranking Technique Based on Interclass Separability for Fuzzy Modeling

This paper presents a modified feature ranking method based on interclass separability for fuzzy modeling. Existing feature selection/ranking techniques are mostly suitable for classification problems. These techniques result in a ranking of the input feature or variables. Our modification exploits an arbitrary fuzzy clustering of the model output data. Using these output clusters, similar feature ranking methods can be used as for classification, where the membership in a cluster (or class) will no longer be crisp, but a fuzzy value determined by the clustering. We propose an iterative algorithm to determine the feature ranking by means of different criterion functions. We examined the proposed method and the criterion functions through a comparative analysis.