Continuous time Bayesian network classifiers

Continuous time naïve Bayes classifier for real-time feedback to neurological patients undergoing motor rehabilitation exercises. Fabio Stella and Yeser Amer. [Display omitted] ► A new class of continuous time Bayesian network classifiers is defined. ► Algorithms for continuous time Bayesian classif...

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Vydané v:Journal of biomedical informatics Ročník 45; číslo 6; s. 1108 - 1119
Hlavní autori: Stella, F., Amer, Y.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Inc 01.12.2012
Predmet:
Algorithms
Bayes Theorem
Bayesian classifiers
Continuous time
Humans
Multivariate trajectory
Pattern Recognition, Automated
Continuous time
Multivariate trajectory
Bayesian classifiers
ISSN:1532-0464, 1532-0480, 1532-0480
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Shrnutí:Continuous time naïve Bayes classifier for real-time feedback to neurological patients undergoing motor rehabilitation exercises. Fabio Stella and Yeser Amer. [Display omitted] ► A new class of continuous time Bayesian network classifiers is defined. ► Algorithms for continuous time Bayesian classifiers are developed. ► Real-time feedback to patients undergoing motor rehabilitation is studied. ► Continuous time naive Bayes outperforms DTW and OE-DTW on motor rehabilitation. 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.
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ISSN:1532-0464
1532-0480
1532-0480
DOI:10.1016/j.jbi.2012.07.002