Abstract
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.
This work was partly supported by the Information Fusion Research Program (University of Skövde, Sweden), in partnership with the Swedish Knowledge Foundation under grant 2010-0320 (http://www.infofusion.se, UMIF project).
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Antonucci, A., Karlsson, A., Sundgren, D. (2014). Decision Making with Hierarchical Credal Sets. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2014. Communications in Computer and Information Science, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-319-08852-5_47
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DOI: https://doi.org/10.1007/978-3-319-08852-5_47
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08851-8
Online ISBN: 978-3-319-08852-5
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