Prototype sample selection based on minimization of the complete cross validation functional

Authors:
M. N. Ivanov
Affiliations:
Moscow Institute of Physics and Technology, Dolgoprudnyi, Moscow oblast, Russia 141700
Venue:
Pattern Recognition and Image Analysis
Year:
2010

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Abstract

A method of prototype sample selection from a training set for a classifier of K nearest neighbors (KNN), based on minimization of the complete cross validation functional, is proposed. The optimization leads to reduction of the training set to the minimum sufficient number of prototypes, removal (censoring) of noise samples, and improvement of the generalization ability, simultaneously.