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Continuous time Bayesian network classifiers.

Abstract：

:The class of continuous time Bayesian network classifiers is defined; it solves the problem of supervised classification on multivariate trajectories evolving in continuous time. The trajectory consists of the values of discrete attributes that are measured in continuous time, while the predicted class is expected to occur in the future. Two instances from this class, namely the continuous time naive Bayes classifier and the continuous time tree augmented naive Bayes classifier, are introduced and analyzed. They implement a trade-off between computational complexity and classification accuracy. Learning and inference for the class of continuous time Bayesian network classifiers are addressed, in the case where complete data are available. A learning algorithm for the continuous time naive Bayes classifier and an exact inference algorithm for the class of continuous time Bayesian network classifiers are described. The performance of the continuous time naive Bayes classifier is assessed in the case where real-time feedback to neurological patients undergoing motor rehabilitation must be provided.

journal_name

J Biomed Inform

journal_title

Journal of biomedical informatics

authors

Stella F,Amer Y

doi

10.1016/j.jbi.2012.07.002

subject

Has Abstract

pub_date

2012-12-01 00:00:00

pages

1108-19

issue

6

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(12)00099-8

journal_volume

45

pub_type

杂志文章

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