Hybrid wrapper-filter approaches for input feature selection using maximum relevance and Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA)

Huda, MD Shamsul, Yearwood, John Leighton and Strainieri, A 2010, Hybrid wrapper-filter approaches for input feature selection using maximum relevance and Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA), in Proceedings - 2010 4th International Conference on Network and System Security, NSS 2010, IEEE, Piscataway, N.J., pp. 442-449, doi: 10.1109/NSS.2010.7.

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Title Hybrid wrapper-filter approaches for input feature selection using maximum relevance and Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA)
Author(s) Huda, MD Shamsul
Yearwood, John LeightonORCID iD for Yearwood, John Leighton orcid.org/0000-0002-7562-6767
Strainieri, A
Conference location Melbourne, Vic.
Conference dates 2010/09/01 - 2010/09/03
Title of proceedings Proceedings - 2010 4th International Conference on Network and System Security, NSS 2010
Publication date 2010
Start page 442
End page 449
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
ISBN 9780769541594
DOI 10.1109/NSS.2010.7
HERDC Research category EN.1 Other conference paper
Persistent URL http://hdl.handle.net/10536/DRO/DU:30101448

Document type: Conference Paper
Collection: Centre for Pattern Recognition and Data Analytics
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