Transfer learning for temporal nodes Bayesian networks

Traditional machine learning algorithms depend heavily on the assumption that there is sufficient data to learn a reliable model. This is not always the case, and in situations where data is limited, transfer learning can be applied to compensate for the lack of information by learning from several sources. In this work, we present a novel methodology for inducing a Temporal Nodes Bayesian Network (TNBN) when training data is scarce by applying a transfer learning strategy. A TNBN is a probabilistic graphical model that offers a compact representation for dynamic domains by defining multiple time intervals in which events can occur. Learning a TNBN poses additional challenges to learning traditional Bayesian networks due to the incorporation of time intervals. Our proposal incorporates novel approaches to transfer knowledge from several TNBNs to learn the structure, parameters and intervals of a target TNBN. To evaluate our algorithm, we performed experiments with a synthetic network, where we created auxiliary models by altering the structure, parameters and temporal intervals of the original model. Results show that the proposed algorithm is capable of retrieving a reliable model even when few records are available for the target domain. We also performed experiments with a real-world data set belonging to the medical domain of HIV, where we were able to learn some documented mutational pathways and their temporal relations by applying transfer learning.