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

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conference contribution
posted on 2003-01-01, 00:00 authored by L Pan, H Zheng, Saeid Nahavandi
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.

History

Event

International Conference on Machine Learning and Cybernetics

Pagination

1185 - 1189

Publisher

IEEE Xplore

Location

China

Place of publication

Piscataway, N.J.

Start date

2003-11-02

End date

2003-11-05

ISBN-13

9780780378650

ISBN-10

0780378652

Language

eng

Notes

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Publication classification

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.

Editor/Contributor(s)

X Wang, D Yeung

Title of proceedings

2003 International Conference on Machine Learning and Cybernetics,

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