continuous time bayesian networks classifiers

Fabio Stella

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.