CTBNCToolkit: Continuous Time Bayesian Network Classifier Toolkit
This toolkit addresses the need for accessible tools in temporal data analysis, but it is incremental as it packages existing methods into a software library without introducing new algorithmic breakthroughs.
The paper introduces CTBNCToolkit, an open-source Java toolkit for continuous time Bayesian network classifiers, which provides algorithms for inference, parameter learning, and structural learning to handle temporal classification of multivariate streaming data where event durations matter and the class remains constant.
Continuous time Bayesian network classifiers are designed for temporal classification of multivariate streaming data when time duration of events matters and the class does not change over time. This paper introduces the CTBNCToolkit: an open source Java toolkit which provides a stand-alone application for temporal classification and a library for continuous time Bayesian network classifiers. CTBNCToolkit implements the inference algorithm, the parameter learning algorithm, and the structural learning algorithm for continuous time Bayesian network classifiers. The structural learning algorithm is based on scoring functions: the marginal log-likelihood score and the conditional log-likelihood score are provided. CTBNCToolkit provides also an implementation of the expectation maximization algorithm for clustering purpose. The paper introduces continuous time Bayesian network classifiers. How to use the CTBNToolkit from the command line is described in a specific section. Tutorial examples are included to facilitate users to understand how the toolkit must be used. A section dedicate to the Java library is proposed to help further code extensions.