Extremely Randomized CNets for Multi-label Classification

  • Teresa M. A. BasileEmail author
  • Nicola Di Mauro
  • Floriana Esposito
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11298)


Multi-label classification (MLC) is a challenging task in machine learning consisting in the prediction of multiple labels associated with a single instance. Promising approaches for MLC are those able to capture label dependencies by learning a single probabilistic model—differently from other competitive approaches requiring to learn many models. The model is then exploited to compute the most probable label configuration given the observed attributes. Cutset Networks (CNets) are density estimators leveraging context-specific independencies providing exact inference in polynomial time. The recently introduced Extremely Randomized CNets (XCNets) reduce the structure learning complexity making able to learn ensembles of XCNets outperforming state-of-the-art density estimators. In this paper we employ XCNets for MLC by exploiting efficient Most Probable Explanations (MPE). An experimental evaluation on real-world datasets shows how the proposed approach is competitive w.r.t. other sophisticated methods for MLC.


Multi-label classification Cutset networks Tractable probabilistic models 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Teresa M. A. Basile
    • 2
    • 3
    Email author
  • Nicola Di Mauro
    • 1
  • Floriana Esposito
    • 1
  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), Bari DivisionBariItaly

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