Bayesian instance selection for the nearest neighbor rule

The nearest neighbors rules are commonly used in pattern recognition and statistics. The performance of these methods relies on three crucial choices: a distance metric, a set of prototypes and a classification scheme. In this paper, we focus on the second, challenging issue: instance selection. We apply a maximum a posteriori criterion to the evaluation of sets of instances and we propose a new optimization algorithm. This gives birth to Eva, a new instance selection method. We benchmark this method on real datasets and perform a multi-criteria analysis: we evaluate the compression rate, the predictive accuracy, the reliability and the computational time. We also carry out experiments on synthetic datasets in order to discriminate the respective contributions of the criterion and the algorithm, and to illustrate the advantages of Eva over the state-of-the-art algorithms. The study shows that Eva outputs smaller and more reliable sets of instances, in a competitive time, while preserving the predictive accuracy of the related classifier.