Notes on the evolution of feature selection methodology
Kybernetika, Tome 43 (2007) no. 5, pp. 713-730 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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The paper gives an overview of feature selection techniques in statistical pattern recognition with particular emphasis on methods developed within the Institute of Information Theory and Automation research team throughout recent years. Besides discussing the advances in methodology since times of Perez’s pioneering work the paper attempts to put the methods into a taxonomical framework. The methods discussed include the latest variants of the optimal algorithms, enhanced sub-optimal techniques and the simultaneous semi- parametric probability density function modelling and feature space selection method. Some related issues are illustrated on real data by means of the Feature Selection Toolbox software.
The paper gives an overview of feature selection techniques in statistical pattern recognition with particular emphasis on methods developed within the Institute of Information Theory and Automation research team throughout recent years. Besides discussing the advances in methodology since times of Perez’s pioneering work the paper attempts to put the methods into a taxonomical framework. The methods discussed include the latest variants of the optimal algorithms, enhanced sub-optimal techniques and the simultaneous semi- parametric probability density function modelling and feature space selection method. Some related issues are illustrated on real data by means of the Feature Selection Toolbox software.
Classification : 62G05, 62H30, 65C60, 68T10
Keywords: feature selection; branch & bound; sequential search; mixture model
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Somol, Petr; Novovičová, Jana; Pudil, Pavel. Notes on the evolution of feature selection methodology. Kybernetika, Tome 43 (2007) no. 5, pp. 713-730. http://geodesic.mathdoc.fr/item/KYB_2007_43_5_a8/

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