Tuesday 8 March 2016

Improving Bayesian networks by learning from similar ones

A major challenge of the BAYES-KNOWLEDGE project is about how to build useful and accurate Bayesian network (BN) models for decision support when there is little relevant data. Much of what we have done involves exploiting expert judgment. But my colleagues Yun Zhou and Tim Hospedales have developed a so-called 'tranfer learning' method that enables us to leverage data from different but related problems. Suppose, for example, we have a BN model for a particular medical diagnostic problem that we built based on limited data and expert judgment in the UK. But suppose also that a model for the same (or very similar) diagnostic problem has been developed in the USA based on a much larger data set. Some assumptions in the US model will be different to the UK (such as the population demographics or particular testing methods) but much of the underlying pathology will be the same. The challenge is to understand and exploit the heterogeneous relatedness of the models.

The result of this work has just been published in an article in the Elsevier journal Expert Systems with Applications. Elsevier have provided free access to the article until April 24, 2016:
The article describes a new transfer learning algorithm for improved BN parameter learning, and the  experimental results demonstrate its superiority compared to other state-of-the-art parameter transfer methods. The method is applied to a real-world medical case study, namely the problem of trauma care (a problem for which our team had initially developed a decision support BN model in collaboration with UK medics).

Full reference for the new article:

Zhou, Y., Hospedales, T., Fenton, N. E. (2016), "When and where to transfer for Bayes net parameter learning", Expert Systems with Applications. 55, 361-373

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