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Digital Library

of the European Council for Modelling and Simulation

 

Title:

Optimization Of Neural Network Inputs By Feature Selection Methods

Authors:

Michal Prochazka, Zuzana Oplatkov√°, Jiri Holoska, Vladimir Gerlich

Published in:

 

(2011).ECMS 2011 Proceedings edited by: T. Burczynski, J. Kolodziej, A. Byrski, M. Carvalho. European Council for Modeling and Simulation. doi:10.7148/2011 

 

ISBN: 978-0-9564944-2-9

 

25th European Conference on Modelling and Simulation,

Jubilee Conference

Krakow, June 7-10, 2011

 

Citation format:

Prochazka, M., Oplatkova, Z., Holoska, J., & Gerlich, V. (2011). Optimization Of Neural Network Inputs By Feature Selection Methods. ECMS 2011 Proceedings edited by: T. Burczynski, J. Kolodziej, A. Byrski, M. Carvalho (pp. 440-445). European Council for Modeling and Simulation. doi:10.7148/2011-0440-0445

DOI:

http://dx.doi.org/10.7148/2011-0440-0445

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.

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