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Performance evaluation of prototype selection algorithms for nearest neighbor classification

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4 Author(s)
J. S. Sanchez ; Univ. Jaume 1, Castellon, Spain ; R. Barandela ; R. Alejo ; A. I. Marques

Prototype selection is primarily effective in improving the classification performance of nearest neighbor (NN) classifier and also partially in reducing its storage and computational requirements. This paper reviews some prototype selection algorithms for NN classification and experimentally evaluates their performance using a number of real data sets. Finally, new approaches based on combining the NN and the nearest centroid neighbor (NCN) of a sample are also introduced

Published in:

Computer Graphics and Image Processing, 2001 Proceedings of XIV Brazilian Symposium on

Date of Conference:

Oct 2001