0357265 - ÚTIA 2012 CZ eng V - Research Report
Somol, Petr - Grim, Jiří
Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition Problems.
Praha: ÚTIA AV ČR, v.v.i, 2011. 9 s. Research Report, 2295.
R&D Projects: GA MŠMT 1M0572; GA MŠMT 2C06019
Institutional research plan: CEZ:AV0Z10750506
Keywords : feature selection, * high dimensionality * ranking * generalization * over-fitting * stability * classification * pattern recognition * machine learning
Subject RIV: BD - Theory of Information
Result website:
http://library.utia.cas.cz/separaty/2011/RO/somol-fast dependency-aware feature selection in very-high-dimensional pattern recognition problems.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0195583