A new hybrid global optimization approach for selecting clinical and biological features that are relevant to the effective diagnosis of ovarian cancer

Alzubaidi, A ORCID: https://orcid.org/0000-0002-5977-564X, Cosma, G ORCID: https://orcid.org/0000-0002-4663-6907, Brown, D ORCID: https://orcid.org/0000-0002-1677-7485 and Pockley, AG ORCID: https://orcid.org/0000-0001-9593-6431, 2016. A new hybrid global optimization approach for selecting clinical and biological features that are relevant to the effective diagnosis of ovarian cancer. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI): Proceedings. Piscataway, NJ: Institute of Electrical and Electronic Engineers. ISBN 9781509042401

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Item Type:	Chapter in book
Creators:	Alzubaidi, A., Cosma, G., Brown, D. and Pockley, A.G.
Publisher:	Institute of Electrical and Electronic Engineers
Place of Publication:	Piscataway, NJ
Date:	2016
ISBN:	9781509042401
Identifiers:	Number Type 10.1109/SSCI.2016.7849954 DOI 16670190 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	16 Mar 2017 15:53
Last Modified:	27 Aug 2021 09:54
URI:	https://irep.ntu.ac.uk/id/eprint/30406

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