Empirical evaluation of the difficulty of finding a good value of k for the nearest neighbor

Authors:
Francisco J. Ferrer-Troyano;Jesús S. Aguilar-Ruiz;José C. Riquelme
Affiliations:
Department of Computer Science, University of Sevilla, Sevilla, Spain;Department of Computer Science, University of Sevilla, Sevilla, Spain;Department of Computer Science, University of Sevilla, Sevilla, Spain
Venue:
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartII
Year:
2003

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Abstract

As an analysis of the classification accuracy bound for the Nearest Neighbor technique, in this work we have studied if it is possible to find a good value of the parameter k for each example according to their attribute values. Or at least, if there is a pattern for the parameter k in the original search space. We have carried out different approaches based on the Nearest Neighbor technique and calculated the prediction accuracy for a group of databases from the UCI repository. Based on the experimental results of our study, we can state that, in general, it is not possible to know a priori a specific value of k to correctly classify an unseen example.