Article,

Co-evolution of Nearest Neighbor Classifiers

, and .
International Journal of Pattern Recognition and Artificial Intelligence, 21 (5): 921--946 (August 2007)
DOI: doi:10.1142/S0218001407005752

Abstract

This paper presents experiments of Nearest Neighbour (NN) classifier design using different evolutionary computation methods. Through multi-objective and co-evolution techniques, it combines genetic algorithms and genetic programming to both select NN prototypes and design a neighbourhood proximity measure, in order to produce a more efficient and robust classifier. The proposed approach is compared with the standard NN classifier, with and without the use of classic prototype selection methods, and classic data normalisation. Results on both synthetic and real data sets show that the proposed methodology performs as well or better than other methods on all tested data sets.

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