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Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition
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SYSNO ASEP 0365937 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition Author(s) Somol, Petr (UTIA-B) RID
Grim, Jiří (UTIA-B) RID, ORCID
Pudil, P. (CZ)Source Title Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2011). - Piscataway : IEEE, 2011 - ISBN 978-1-4577-0653-0 Pages s. 502-509 Number of pages 8 s. Publication form CD-ROM - CD-ROM Action The 2011 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2011) Event date 09.10.2011-12.10.2011 VEvent location Anchorage, Alaska Country US - United States Event type WRD Language eng - English Country US - United States Keywords feature selection ; high dimensionality ; ranking ; classification ; machine learning Subject RIV IN - Informatics, Computer Science R&D Projects 1M0572 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10750506 - UTIA-B (2005-2011) UT WOS 000298615100102 DOI 10.1109/ICSMC.2011.6083733 Annotation The paper addresses the problem of making dependency-aware feature selection feasible in pattern recognition problems of very high dimensionality. The idea of individually best ranking is generalized to evaluate the contextual quality of each feature in a series of randomly generated feature subsets. Each random subset is evaluated by a criterion function of arbitrary choice (permitting functions of high complexity). Eventually, the novel dependency-aware feature rank is computed, expressing the average benefit of including a feature into feature subsets. The method is efficient and generalizes well especially in very-high-dimensional problems, where traditional context-aware feature selection methods fail due to prohibitive computational complexity or to over-fitting. The method is shown well capable of over-performing the commonly applied individual ranking which ignores important contextual information contained in data. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2012
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