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

Alzubaidi, A., Cosma, G. ORCID: 0000-0002-4663-6907, Brown, D. ORCID: 0000-0002-1677-7485 and Pockley, A.G. ORCID: 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

Full text not available from this repository.
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
Identifiers:
NumberType
10.1109/SSCI.2016.7849954DOI
16670190Other
Divisions: Schools > School of Science and Technology
Depositing User: Jonathan Gallacher
Date Added: 16 Mar 2017 15:53
Last Modified: 09 Jun 2017 14:13
URI: http://irep.ntu.ac.uk/id/eprint/30406

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