Original title: Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition Problems
Authors: Somol, Petr ; Grim, Jiří
Document type: Research reports
Year: 2011
Language: eng
Series: Research Report, volume: 2295
Abstract: 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.
Keywords: classification; feature selection,; generalization; high dimensionality; machine learning; over-fitting; pattern recognition; ranking; stability
Project no.: CEZ:AV0Z10750506 (CEP), 1M0572 (CEP), 2C06019 (CEP)
Funding provider: GA MŠk, GA MŠk

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2011/RO/somol-fast dependency-aware feature selection in very-high-dimensional pattern recognition problems.pdf
Original record: http://hdl.handle.net/11104/0195583

Permalink: http://www.nusl.cz/ntk/nusl-42716


The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Reports > Research reports