We present a novel algorithm for computing a training set consistent subset for the nearest neighbor decision rule. The algorithm, called FCNN rule, has some desirable properties. Indeed, it is order independent and has subquadratic worst case time complexity, while it requires few iterations to converge, and it is likely to select points very close to the decision boundary. We compare the FCNN rule with state of the art competence preservation algorithms on large multidimensional training sets, showing that it outperforms existing methods in terms of learning speed and learning scaling behavior, and in terms of size of the model, while it guarantees a comparable prediction accuracy. 1
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