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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Filtering as a Tool of Diversity in Ensemble of Classifiers
verfasst von : Eva Volna, Martin Kotyrba, Vaclav Kocian
Erschienen in: Industrial Engineering, Management Science and Applications 2015
Verlag: Springer Berlin Heidelberg
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This paper discusses possibilities of using ensembles of neural-networks-based classifiers in pattern recognition and classification. Attention is paid to systems that minimize demands on data preprocessing. Minimizing of requirements for preprocessing leads automatically to systems that are able to sufficiently classify the submitted data into predefined classes without knowledge of details of their significance. In our experiment, we try to increase diversity of classifiers by various filtering methods. The methods proposed in this paper come out from a technique called boosting, which is based on the principle of combining a large number of so-called weak classifiers into a strong classifier. All proposed improvements are experimentally verified.