Filtering as a Tool of Diversity in Ensemble of Classifiers

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F15%3AA1601EAW" target="_blank" >RIV/61988987:17310/15:A1601EAW - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Filtering as a Tool of Diversity in Ensemble of Classifiers
Original language description
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.
Czech name
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Czech description
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Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Industrial Engineering, Management Science and Applications 2015, Lecture Notes in Electrical Engineering
ISBN
978-3-662-47199-9
ISSN
1876-1100
e-ISSN
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Number of pages
11
Pages from-to
767-777
Publisher name
Springer Berlin Heidelberg
Place of publication
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Event location
Tokyo, Japan
Event date
May 26, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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