Constructing fuzzy classification systems from weighted training patterns

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

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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
Creators: Nakashima, T., Ishibuchi, H. and Bargiela, A.
Publisher: IEEE
Place of Publication: Piscataway, NJ, USA
Date: 2004
Volume: 3
Divisions: Schools > School of Science and Technology
Depositing User: EPrints Services
Date Added: 09 Oct 2015 11:18
Last Modified: 19 Oct 2015 14:44
URI: http://irep.ntu.ac.uk/id/eprint/25719

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