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A feature ranking technique based on interclass separability for fuzzy modeling

Tikk, D. and Wong, K.W. (2007) A feature ranking technique based on interclass separability for fuzzy modeling. In: 5th IEEE International Conference on Computational Cybernetics (ICCC 2007), 19-21 Oct. 2007, Gammarth, Tunisia pp. 251-256.

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    Link to Published Version: http://dx.doi.org/10.1109/ICCCYB.2007.4402044
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    Abstract

    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.

    Publication Type: Conference Paper
    Murdoch Affiliation: School of Information Technology
    Publisher: IEEE
    Copyright: © 2007 IEEE.
    Notes: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    URI: http://researchrepository.murdoch.edu.au/id/eprint/977
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