Abstract
The naive credal classifier (NCC) extends naive Bayes classifier (NBC) to imprecise probabilities to robustly deal with the specification of the prior; NCC models a state of ignorance by using a set of priors, which is formalized by Walley’s Imprecise Dirichlet Model (IDM). NCC has been shown to return more robust classification than NBC. However, there are particular situations (which we precisely characterize in the paper) under which the extreme densities included by the IDM force NCC to become very indeterminate, although NBC is able to issue accurately classifications. In this paper, we propose two approaches which overcome this issue, by restricting the set of priors of the IDM . We analyze both approaches theoretically and experimentally.
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Corani, G., Benavoli, A. (2010). Restricting the IDM for Classification. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods. IPMU 2010. Communications in Computer and Information Science, vol 80. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14055-6_34
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DOI: https://doi.org/10.1007/978-3-642-14055-6_34
Publisher Name: Springer, Berlin, Heidelberg
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