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The application of rough set and Kohonen network to feature selection for object extraction

Pan, Li, Zheng, Hong and Nahavandi, Saeid 2003, The application of rough set and Kohonen network to feature selection for object extraction, in 2003 International Conference on Machine Learning and Cybernetics,, IEEE Xplore, Piscataway, N.J., pp. 1185-1189.

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Title The application of rough set and Kohonen network to feature selection for object extraction
Author(s) Pan, Li
Zheng, Hong
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name International Conference on Machine Learning and Cybernetics
Conference location China
Conference dates November 2 - 5 2003
Title of proceedings 2003 International Conference on Machine Learning and Cybernetics,
Editor(s) Wang, Xizhao
Yeung, Daniel
Publication date 2003
Start page 1185
End page 1189
Publisher IEEE Xplore
Place of publication Piscataway, N.J.
Keyword(s) rough set
feature selection
Kohonen network
Summary Selecting a set of features which is optimal for a given task is a problem which plays an important role in a wide variety of contexts including pattern recognition, images understanding and machine learning. The paper describes an application of rough sets method to feature selection and reduction in texture images recognition. The proposed methods include continuous data discretization based on Kohonen neural network and maximum covariance, and rough set algorithms for feature selection and reduction. The experiments on trees extraction from aerial images show that the methods presented in this paper are practical and effective.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 0780378652
9780780378650
Language eng
Field of Research 090999 Geomatic Engineering not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30009618

Document type: Conference Paper
Collections: School of Engineering and Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.