Restricting the IDM for Classification

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Restricting the IDM for Classification
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.
Giorgio Corani, Alessio Benavoli
Added 13 Feb 2011
Updated 13 Feb 2011
Type Journal
Year 2010
Where IPMU
Authors Giorgio Corani, Alessio Benavoli
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