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Digital Library of the
European Council for Modelling and Simulation |
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Title: |
Optimization
Of Neural Network Inputs By Feature Selection Methods |
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Authors: |
Michal Prochazka, Zuzana Oplatková, Jiri Holoska, Vladimir Gerlich |
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Published in:
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(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
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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 |
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DOI: |
http://dx.doi.org/10.7148/2011-0440-0445 |
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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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