0357265 20240103194938.120110404235959.9 Praha ÚTIA AV ČR, v.v.i 2011 9 s. Research Report 2295 feature selection, high dimensionality ranking generalization over-fitting stability classification pattern recognition 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. http://library.utia.cas.cz/separaty/2011/RO/somol-fast dependency-aware feature selection in very-high-dimensional pattern recognition problems.pdf 1M0572 GA MŠk cav_un_auth*0001814 2C06019 GA MŠk 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. 2012 BD 4 O 4o 20231122134453.4 http://hdl.handle.net/11104/0195583 2011 Soubory v repozitáři: 0357265.pdf 2011 Praha ÚTIA AV ČR, v.v.i