A hybrid classifier based on boxes and nearest neighbors

In this paper we analyse the generalization performance of a type of binary classifier defined on the unit cube. This classifier combines some of the aspects of the standard methods that have been used in the logical analysis of data (LAD) and geometric classifiers, with a nearest-neighbor paradigm. We assess the predictive performance of the new classifier in learning from a sample, obtaining generalization error bounds that improve as a measure of `robustness' of the classifier on the training sample increases.