A classification based scoring function for continuous time Bayesian network classifiers

Continuous time Bayesian network classifiers are designed for analyzing multivariate streaming data when time duration of events matters. New continuous time Bayesian network classifiers are introduced while their conditional log-likelihood scoring function is developed. A learning algorithm, combining conditional log-likelihood with Bayesian parameter estimation is developed. Classification accuracies achieved on synthetic data by continuous time and dynamic Bayesian network classifiers are compared. Results show that conditional log-likelihood scor- ing combined with Bayesian parameter estimation outperforms marginal log-likelihood scoring in terms of classification accuracy. Continuous time Bayesian network classifiers are applied to post-stroke rehabilitation.