Feature Ranking Based on Interclass Separability for Fuzzy Control Application∗
نویسندگان
چکیده
This paper presents a modified feature ranking method on interclass separability based for fuzzy control application. 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 control output data. Using these output clusters similar feature selection methods can be used as for classification, where the membership in a class (or cluster) will no longer be crisp, but a fuzzy value determined by the clustering. We studied the proposed method through a comparative analysis.
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