Bruno Borsato,  Luiz Merschmann,  Alexandre Plastino. 

The k-NN (k Nearest Neighbours) classification technique is characterized by its simplicity and efficient performance. However, the good performance of this method relies on the choice of an appropriate value for the input parameter k. In this work, we propose a method to estimate an adequate value for parameter k for any given database. Experimental results have shown that, in terms of predictive accuracy, k-NN using the estimated value for k usually outperforms k-NN with the values commonly used for k, as well as methods such as decision trees and naive Bayesian classification.

http://www.lbd.dcc.ufmg.br:8080/colecoes/waamd/2007/003.pdf

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