This article is cited in 1 paper

Selecting informative variables in the identification problem

Eugene D. Mihov, Oleg V. Nepomnyashchiy

Institute of Space and Information Technology, Siberian Federal University, Kirensky, 26, Krasnoyarsk, 660074, Russia

Abstract: The problem of multidimensional object classification with small training sample is considered. The following algorithms of estimating variable informativeness are considered: Ad, Del, AdDel.
A new algorithm for selecting informative variables is proposed. It is based on the optimization of the coefficient vector of the kernel fuzziness. Some modification of this algorithm is also discussed.
The comparative analysis of existing methods for selecting informative variables is presented.

Keywords: classification, small training sample, informative variable, optimization of the coefficient vector of the kernel fuzziness.

UDC: 519.87

Received: 23.06.2016
Received in revised form: 14.08.2016
Accepted: 14.09.2016

Language: English

DOI: 10.17516/1997-1397-2016-9-4-473-480

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