Selecting Informative Variables in the Identification Problem

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.