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Learning continuous time Bayesian network classifiers. (English) Zbl 1433.68335
Summary: Streaming data are relevant to finance, computer science, and engineering while they are becoming increasingly important to medicine and biology. Continuous time Bayesian network classifiers are designed for analyzing multivariate streaming data when time duration of event matters. Structural and parametric learning for the class of continuous time Bayesian network classifiers are considered in the case where complete data is available. Conditional log-likelihood scoring is developed for structural learning on continuous time Bayesian network classifiers. Performance of continuous time Bayesian network classifiers learned when combining conditional log-likelihood scoring and Bayesian parameter estimation are compared with that achieved by continuous time Bayesian network classifiers when learning is based on marginal log-likelihood scoring and to that achieved by dynamic Bayesian network classifiers. Classifiers are compared in terms of accuracy and computation time. Comparison is based on numerical experiments where synthetic and real data are used. Results show that conditional log-likelihood scoring combined with Bayesian parameter estimation outperforms marginal log-likelihood scoring. Conditional log-likelihood scoring becomes even more effective when the amount of available data is limited. Continuous time Bayesian network classifiers outperform in terms of computation time and accuracy dynamic Bayesian network on synthetic and real data sets.
Reviewer: Reviewer (Berlin)

68T05 Learning and adaptive systems in artificial intelligence
62F15 Bayesian inference
62H30 Classification and discrimination; cluster analysis (statistical aspects)
BNT; Ctbnctoolkit; REVEAL
Full Text: DOI
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