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Optimization of neural network inputs by feature selection methods

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dc.title Optimization of neural network inputs by feature selection methods en
dc.contributor.author Procházka, Michal
dc.contributor.author Oplatková, Zuzana
dc.contributor.author Hološka, Jiří
dc.contributor.author Gerlich, Vladimír
dc.relation.ispartof Proceedings - 25th European Conference on Modelling and Simulation, ECMS 2011
dc.identifier.isbn 9780956494429
dc.date.issued 2011
dc.citation.spage 440
dc.citation.epage 445
dc.event.title 25th European Conference on Modelling and Simulation, ECMS 2011
dc.event.location Krakow
utb.event.state-en Poland
utb.event.state-cs Polsko
dc.event.sdate 2011-06-07
dc.event.edate 2011-06-10
dc.type conferenceObject
dc.language.iso en
dc.identifier.doi 10.7148/2011-0440-0445
dc.relation.uri http://www.scs-europe.net/dlib/2011/2011-0440.htm
dc.relation.uri http://www.scs-europe.net/conf/ecms2011/ecms2011%20accepted%20papers/is_ECMS_0109.pdf
dc.subject Artificial neural networks en
dc.subject Dimension reduction en
dc.subject Feature selection en
dc.subject Steganalysis en
dc.description.abstract The main idea of this paper is to compare feature selection methods for dimension reduction of the original dataset to reach optimization of steganalysis process by artificial neural networks (ANN). Feature selection methods are tools based on statistic exploited in pre-processing step of data mining workflow. These methods are very useful in a dimension reduction, removing of insignificant data, increasing comprehensibility and learning accuracy. Dimension reduction leads to reduced computational resource consumptions, which is validated by ANN simulations. Steganalysis is a field of the computer security, which deals with a discovering of hidden information in images which is normally unrecognizable. All dataming processes, which reduce the dimension of ANN input layer, should keep accuracy of steganalysis on the original level. © ECMS. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1004834
utb.identifier.obdid 43874199
utb.identifier.scopus 2-s2.0-84857761709
utb.identifier.wok 000392767500067
utb.source d-scopus
dc.date.accessioned 2015-06-04T12:55:42Z
dc.date.available 2015-06-04T12:55:42Z
dc.description.sponsorship internal grant agency of Tomas Bata University in Zlin [IGA/44/FAI/10/D]; Ministry of Education of the Czech Republic [MSM 7088352101]; Grant Agency of Czech Republic [GACR 102/09/1680]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]
utb.contributor.internalauthor Procházka, Michal
utb.contributor.internalauthor Oplatková, Zuzana
utb.contributor.internalauthor Hološka, Jiří
utb.contributor.internalauthor Gerlich, Vladimír
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