ROC Analysis of FCM Classifier With Cauchy Weight

Hidetomo Ichihashi; Katsuhiro Honda; Fumiaki Matsuura

doi:10.14864/softscis.2006.0.1912.0

SCIS & ISIS 2006

Session ID : SA-F3-4

DOI https://doi.org/10.14864/softscis.2006.0.1912.0

Conference information

Host: Japan SOciety for Fuzzy Theory and intelligent informatics

Co-host: The Korea Fuzzy Logic and Intelligent Systems Society, IEEE Computational Intelligence Society, The International Fuzzy Systems Association, 21th Century COE Program "Creation of Agent-Based Social Systems Sciences"

SA-F3 Clustering (2)

ROC Analysis of FCM Classifier With Cauchy Weight

*Hidetomo Ichihashi, Katsuhiro Honda, Fumiaki Matsuura

Author information

Keywords: ROC, IRLS, rejection

CONFERENCE PROCEEDINGS FREE ACCESS

Details

Abstract

A postsupervised FCMclassifier using a modified Cauchy weight as a membership function has been proposed. The performances of the classifier in terms of ROC and rejection curves are compared by using the benchmark datasets of the UCI ML repository. Cauchy weight function gives lower confidence to the classification decision and this distinguishes the FCM classifier from the Gaussian classifier. The FCM classifier outperforms the Gaussian classifier and the k-nearest neighbor classifier on many datasets in general.

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