We elaborate on hierarchical credal sets, which are sets of probability mass functions paired with second-order distributions. A new criterion to make decisions based on these models is proposed. This is achieved by sampling from the set of mass functions and considering the Kullback-Leibler divergence from the weighted center of mass of the set. We evaluate this criterion in a simple classification scenario: the results show performance improvements when compared to a credal classifier where the second-order distribution is not taken into account.


Credal sets second-order models hierarchical credal sets shifted Dirichlet distribution credal classification decision making 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alessandro Antonucci
    • 1
  • Alexander Karlsson
    • 2
  • David Sundgren
    • 3
  1. 1.Istituto Dalle Molle di Studi sull’Intelligenza ArtificialeManno-LuganoSwitzerland
  2. 2.Informatics Research CenterUniversity of SkövdeSweden
  3. 3.Department of Computer and Systems SciencesStockholm UniversitySweden

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