Nakashima, T, Ishibuchi, H and Bargiela, A, 2004. Constructing fuzzy classification systems from weighted training patterns. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Piscataway, NJ, USA: IEEE, pp. 2386-2391. ISBN 0780385675
Full text not available from this repository.Abstract
In this paper, we examine the effect of weighting training patterns on the performance of fuzzy rule-based classification systems. A weight is assigned to each given pattern based on the class distribution of its neighboring given patterns. The values of weights are determined proportionally by the number of neighboring patterns from the same class. Large values are assigned to given patterns with many patterns from the same class. Patterns with small weights are not considered in the generation of fuzzy rule-based classification systems. That is, fuzzy if-then rules are generated from only patterns with large weights. These procedures can be viewed as preprocessing in pattern classification. The effect of weighting is examined for an artificial data set and several real-world data sets
Item Type: | Chapter in book |
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Creators: | Nakashima, T., Ishibuchi, H. and Bargiela, A. |
Publisher: | IEEE |
Place of Publication: | Piscataway, NJ, USA |
Date: | 2004 |
Volume: | 3 |
ISBN: | 0780385675 |
Divisions: | Schools > School of Science and Technology |
Record created by: | EPrints Services |
Date Added: | 09 Oct 2015 11:18 |
Last Modified: | 19 Oct 2015 14:44 |
URI: | https://irep.ntu.ac.uk/id/eprint/25719 |
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