Corani, Giorgio and Benavoli, Alessio (2010) Restricting the IDM for classification. In: Information Processing and Management of Uncertainty in Knowledge-based Systems. Theory and Methods. Communications in Computer and Information Science, 80 . Springer, Berlin / Heidelberg, pp. 328-337. ISBN 978-3-642-14054-9
Full text not available from this repository.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.
| Item Type: | Chapter in book |
|---|---|
| Subjects: | Computer sciences > Artificial intelligence > Machine learning |
| Department/unit: | Dipartimento tecnologie innovative > Istituto Dalle Molle di studi sull’intelligenza artificiale USI-SUPSI |
| Depositing User: | Giorgio Corani |
| Date Deposited: | 12 May 2014 09:31 |
| Last Modified: | 28 Apr 2016 14:20 |
| URI: | http://repository.supsi.ch/id/eprint/4723 |
Actions (login required)
 |
View Item |