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Improving Sequential Feature Selection Methods Performance by Means of Hybridization
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SYSNO ASEP 0341554 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Improving Sequential Feature Selection Methods Performance by Means of Hybridization Author(s) Somol, Petr (UTIA-B) RID
Novovičová, Jana (UTIA-B)
Pudil, Pavel (UTIA-B) RIDSource Title Proc. 6th IASTED Int. Conf. on Advances in Computer Science and Engineering. - Calgary : ACTA Press, 2010 / Rafea - ISBN 978-0-88986-830-4 Pages 689-1-689-10 Number of pages 10 s. Publication form www - www Action Advances in Computer Science and Engineering Event date 15.03.2010-17.03.2010 VEvent location Sharm El Sheikh Country EG - Egypt Event type WRD Language eng - English Country CA - Canada Keywords Feature selection ; sequential search ; hybrid methods ; classification performance ; subset search ; statistical pattern recognition Subject RIV BD - Theory of Information R&D Projects 1M0572 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) GA102/08/0593 GA ČR - Czech Science Foundation (CSF) GA102/07/1594 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10750506 - UTIA-B (2005-2011) Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2011
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