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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Nojima, Y. Nishikawa, S. Ishibuchi, H. |
| Copyright Year | 2011 |
| 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.) |
| 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. |
| Starting Page | 264 |
| Ending Page | 271 |
| File Size | 270041 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424473151 |
| ISSN | 10987584 |
| e-ISBN | 9781424473175 |
| e-ISBN | 9781424473168 |
| DOI | 10.1109/FUZZY.2011.6007739 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-27 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| 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 |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Theoretical Computer Science Software |
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