Hybrid wrapper-filter approaches for input feature selection using maximum relevance-minimum redundancy and artificial neural network input gain measurement approximation (ANNIGMA)

Huda, MD Shamsul, Yearwood, John Leighton and Stranieri, Andrew 2011, Hybrid wrapper-filter approaches for input feature selection using maximum relevance-minimum redundancy and artificial neural network input gain measurement approximation (ANNIGMA), in ACSC 2011 :, Australian Computer Society, Sydney, N.S.W., pp. 43-52.

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Title Hybrid wrapper-filter approaches for input feature selection using maximum relevance-minimum redundancy and artificial neural network input gain measurement approximation (ANNIGMA)
Author(s) Huda, MD ShamsulORCID iD for Huda, MD Shamsul orcid.org/0000-0001-7848-0508
Yearwood, John LeightonORCID iD for Yearwood, John Leighton orcid.org/0000-0002-7562-6767
Stranieri, Andrew
Conference name Australian Computer Society. Conference (34th : 2011 : Perth, W.A.)
Conference location Perth, W.A.
Conference dates 2011/01/17 - 2011/01/20
Title of proceedings ACSC 2011 :
Editor(s) Reynolds, Mark
Publication date 2011
Series Australian Computer Society Conference
Start page 43
End page 52
Total pages 10
Publisher Australian Computer Society
Place of publication Sydney, N.S.W.
Keyword(s) hybrid feature selection
wrapper
filter maximum-relevance
maximum-relevance and minimum redundancy
ANNIGMA wrapper
ISBN 9781920682934
ISSN 1445-1336
Language eng
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2011, Australian Computer Society, Inc.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30100715

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