Density Estimators for Positive-Unlabeled Learning

  • Teresa M. A. Basile
  • Nicola Di Mauro
  • Floriana Esposito
  • Stefano Ferilli
  • Antonio Vergari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10785)

Abstract

Positive-Unlabeled (PU) learning works by considering a set of positive samples, and a (usually larger) set of unlabeled ones. This challenging setting requires algorithms to cleverly exploit dependencies hidden in the unlabeled data in order to build models able to accurately discriminate between positive and negative samples. We propose to exploit probabilistic generative models to characterize the distribution of the positive samples, and to label as reliable negative samples those that are in the lowest density regions with respect to the positive ones. The overall framework is flexible enough to be applied to many domains by leveraging tools provided by years of research from the probabilistic generative model community. Results on several benchmark datasets show the performance and flexibility of the proposed approach.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Bari “Aldo Moro”BariItaly
  2. 2.Department of PhysicsUniversity of Bari “Aldo Moro”BariItaly
  3. 3.National Institute for Nuclear Physics (INFN)BariItaly

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