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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 thisdoi:10.1007/s10994-010-5170-2 fatcat:ga5qytsttnb3pfpkvozse36k2e