Počet záznamů: 1

Improving Sequential Feature Selection Methods Performance by Means of Hybridization

  1. 1.
    0341554 - UTIA-B 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 MŠk 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 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