Classification Based on Prototypes Generated with Fuzzy C-means Clustering and Differential Evolution

In this paper we propose a simple and effective combined classifier based on the data reduction carried-out through applying fuzzy C-means clustering and differential evolution techniques. The idea is to produce clusters from the training set instances applying fuzzy C-means algorithm. In further st...

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Bibliographic Details
Published inAdvanced Techniques for Knowledge Engineering and Innovative Applications pp. 177 - 188
Main Authors Jędrzejowicz, Joanna, Jędrzejowicz, Piotr
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2013
SeriesCommunications in Computer and Information Science
Subjects
combined classifier
differential evolution
evolutionary algorithms
fuzzy C-means clustering
machine learning
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ISBN9783642420160
3642420168
ISSN1865-0929
1865-0937
DOI10.1007/978-3-642-42017-7_13

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Summary:In this paper we propose a simple and effective combined classifier based on the data reduction carried-out through applying fuzzy C-means clustering and differential evolution techniques. The idea is to produce clusters from the training set instances applying fuzzy C-means algorithm. In further step cluster centroids are used as seeds in the differential evolution algorithm to construct prototypes, each representing a single cluster. Simple distance-based weak classifiers are then used to produce the AdaBoost combined classifier. The approach has been validated experimentally. Computational experiment results confirm good quality of the proposed classifier.
ISBN:9783642420160
3642420168
ISSN:1865-0929
1865-0937
DOI:10.1007/978-3-642-42017-7_13