0365937 20240111140802.220111101235959.9 000298615100102 10.1109/ICSMC.2011.6083733 Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition 8 s. CD-ROM cav_un_epca*0365984978-1-4577-0653-0Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2011)502-509PiscatawayIEEE2011 feature selection high dimensionality ranking classification machine learning cav_un_auth*0101197 Somol Petr Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101091 Grim Jiří Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0021092 Pudil P. CZ pdf http://library.utia.cas.cz/separaty/2011/RO/somol-fast dependency-aware feature selection in very-high-dimensional pattern recognition-c.pdf 1M0572 GA MŠk cav_un_auth*0001814 2C06019 GA MŠk CZ cav_un_auth*0216518 CEZ:AV0Z10750506 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. cav_un_auth*0275416 The 2011 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2011) Anchorage, Alaska 09.10.2011-12.10.2011 US 2012 IN http://hdl.handle.net/11104/0201063 n.a. Proceedings Paper Computer Science Artificial Intelligence|Computer Science Cybernetics|Computer Science Information Systems 2011 000298615100102 WOS pdf cav_un_epca*0365984 Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2011) 978-1-4577-0653-0 502 509 Piscataway IEEE 2011