Fuzzy c-Means Classifier Based on Iteratively Reweighted Least Square Technique

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概要

• 論文の詳細を見る

• A novel membership function and a fuzzy clustering derived from a viewpoint of iteratively reweighted least square (IRLS) techniques resolves the problem of singularity in the regular fuzzy c-means (FCM) clustering. A FCM classifier using the membership function and Mahalanobis distances makes class memberships of outliers less clear-cut, which thus resolve the problem of classification based on normal populations or normal mixtures. Computational experiments show high classification performance and compression rate on several well-known benchmark data sets.

• バイオメディカル・ファジィ・システム学会の論文
• 2005-10-29

著者

• Ichihashi Hidetomo

  Graduate School Of Engineering Osaka Prefecture University

• Honda Katsuhiro

  Graduate School of Engineering, Osaka Prefecture University

• Honda Katsuhiro

  Graduate School Of Engineering Osaka Prefecture University

関連論文

• Fuzzy c-Means Classifier Based on Iteratively Reweighted Least Square Technique

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