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Author Nojima, Y. ♦ Nishikawa, S. ♦ Ishibuchi, H.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2011
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Complexity theory ♦ Genetics ♦ Error analysis ♦ Training ♦ Tuning ♦ Fuzzy sets ♦ Measurement uncertainty ♦ knowledge acquisition ♦ fuzzy classifier design ♦ data complexity measures ♦ meta-learning ♦ pattern classification
Abstract Tens of thousands of classifiers have been proposed so far. There is no best classifier among them for all the existing data sets. The performance of each classifier often depends on the data sets used for comparison. Even for a single classifier, suitable parameters of the classifier also depend on the data sets. That is, there is a possibility that a suited classifier and its parameter specification can be chosen beforehand if the target data sets or their characteristics were known. In recent years, a number of data complexity measures have been proposed to characterize data sets. The aim of this study is to develop a meta-classifier for selecting an appropriate classifier and/or its appropriate parameter specification by means of data complexity measures. In this paper, we focus on the parameter specification of fuzzy classifiers using data complexity measures as a preliminary study. To construct a meta-classifier, we generate a large number of artificial data sets from Keel benchmark data sets. Then we generate meta-patterns which are composed of the values of data complexity measures as inputs and an appropriate fuzzy partition as an output. Using meta-patterns, a meta-classifier is designed by multiobjective genetic fuzzy rule selection. We evaluate the proposed method through leave one-group out cross-validation.
Description Author affiliation: Dept. of Computer Science and Intelligent Systems, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka, 599-8531, Japan (Nojima, Y.; Nishikawa, S.; Ishibuchi, H.)
ISBN 9781424473151
ISSN 10987584
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2011-06-27
Publisher Place Taiwan
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781424473175
Size (in Bytes) 263.71 kB
Page Count 8
Starting Page 264
Ending Page 271


Source: IEEE Xplore Digital Library