A Feature Ranking Algorithm for Fuzzy Modelling Problems

This paper presents a feature ranking method adapted to fuzzy modelling with output from a continuous range. Existing feature selection/ranking techniques are mostly suitable for classification problems, where the range of the output is discrete. These techniques result in a ranking of the input feature (variables). Our approach 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 the application of the Sequential Backward Selection (SBS) search method to determine the feature ranking by means of different criterion functions. We examined the proposed method and the criterion functions through a comparative analysis.