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Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition Problems
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SYSNO ASEP 0357265 Druh ASEP V - Výzkumná zpráva Zařazení RIV Záznam nebyl označen do RIV Název Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition Problems Tvůrce(i) Somol, Petr (UTIA-B) RID
Grim, Jiří (UTIA-B) RID, ORCIDVyd. údaje Praha: ÚTIA AV ČR, v.v.i, 2011 Edice Research Report Č. sv. edice 2295 Poč.str. 9 s. Jazyk dok. eng - angličtina Země vyd. CZ - Česká republika Klíč. slova feature selection, ; high dimensionality ; ranking ; generalization ; over-fitting ; stability ; classification ; pattern recognition ; machine learning Vědní obor RIV BD - Teorie informace CEP 1M0572 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy 2C06019 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy CEZ AV0Z10750506 - UTIA-B (2005-2011) Anotace 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. Pracoviště Ústav teorie informace a automatizace Kontakt Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Rok sběru 2012
Počet záznamů: 1