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 to 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.

Codecasa, D., Stella, F. (2014). Learning Continuous Time Bayesian Network Classifiers. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 55(8), 1728-1746 [10.1016/j.ijar.2014.05.005].

Learning Continuous Time Bayesian Network Classifiers

CODECASA, DANIELE;STELLA, FABIO ANTONIO
2014

Abstract

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 to 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.
Articolo in rivista - Articolo scientifico
Continuous time Bayesian networks; stream data; classification
English
2014
55
8
1728
1746
none
Codecasa, D., Stella, F. (2014). Learning Continuous Time Bayesian Network Classifiers. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 55(8), 1728-1746 [10.1016/j.ijar.2014.05.005].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/51513
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