Improving Sequential Feature Selection Methods Performance by Means of Hybridization

0341554 - ÚTIA 2011 RIV CA eng C - Konferenční příspěvek (zahraniční konf.)
Somol, Petr - Novovičová, Jana - Pudil, Pavel
Improving Sequential Feature Selection Methods Performance by Means of Hybridization.
Proc. 6th IASTED Int. Conf. on Advances in Computer Science and Engineering. Calgary: ACTA Press, 2010 - (Rafea), 689-1-689-10. ISBN 978-0-88986-830-4.
[Advances in Computer Science and Engineering. Sharm El Sheikh (EG), 15.03.2010-17.03.2010]
Grant CEP: GA MŠMT 1M0572; GA ČR GA102/08/0593; GA ČR GA102/07/1594
Grant ostatní: GA MŠk(CZ) 2C06019
Výzkumný záměr: CEZ:AV0Z10750506
Klíčová slova: Feature selection * sequential search * hybrid methods * classification performance * subset search * statistical pattern recognition
Kód oboru RIV: BD - Teorie informace
http://library.utia.cas.cz/separaty/2010/RO/somol-improving sequential feature selection methods performance by means of hybridization.pdf

In this paper we propose the general scheme of defining hybrid feature selection algorithms based on standard sequential search with the aim to improve feature selection performance, especially on high-dimensional or large-sample data. We show experimentally that “hybridization” has not only the potential to dramatically reduce FS search time, but in some cases also to actually improve classifier generalization, i.e., its classification performance on previously unknown data.
Trvalý link: http://hdl.handle.net/11104/0184495