Stefano Basta

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In this paper we present an unsupervised distance-based outlier detection method designed to learn a model over the objects contained in a data set. The learned model, called <i>solving set</i>, is a small subset of the data set that is used to classify new unseen objects as outliers or not. We provide an algorithm that computes a solving set with(More)
In this work we propose a method for computing a minimum size training set consistent subset for the Nearest Neighbor rule (also said CNN problem) via SAT encodings. We introduce the SAT–CNN algorithm, which exploits a suitable encoding of the CNN problem in a sequence of SAT problems in order to exactly solve it, provided that enough computational(More)