Corani, Giorgio and Antonucci, Alessandro and Zaffalon, Marco
(2012)
*
Bayesian networks with imprecise probabilities: theory and application to classification.
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In:
Data Mining: Foundations and Intelligent Paradigms.
Intelligent Systems Reference Library, 23
.
Springer, Berlin / Heidelberg, pp. 49-93.
ISBN 978-3-642-23165-0

## Abstract

Bayesian networks are powerful probabilistic graphical models for modelling uncertainty. Among others, classification represents an important application: some of the most used classifiers are based on Bayesian networks. Bayesian networks are precise models: exact numeric values should be provided for quantification. This requirement is sometimes too narrow. Sets instead of single distributions can provide a more realistic description in these cases. Bayesian networks can be generalized to cope with sets of distributions. This leads to a novel class of imprecise probabilistic graphical models, called credal networks. In particular, classifiers based on Bayesian networks are generalized to so-called credal classifiers. Unlike Bayesian classifiers, which always detect a single class as the one maximizing the posterior class probability, a credal classifier may eventually be unable to discriminate a single class. In other words, if the available information is not sufficient, credal classifiers allow for indecision between two or more classes, this providing a less informative but more robust conclusion than Bayesian classifiers.

Item Type: | Chapter in book |
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Subjects: | Mathematical sciences > Statistics > Statistical modelling Computer sciences > Artificial intelligence |

Depositing User: | Giorgio Corani |

Date Deposited: | 12 May 2014 09:30 |

Last Modified: | 28 Apr 2016 14:14 |

URI: | http://repository.supsi.ch/id/eprint/4720 |

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