Bayesian networks can be built based on knowledge, data, or both. Independent of the source of information used to build the model, inaccuracies might occur or the application domain might change. Therefore, there is a need to continuously improve the model during its usage. As new data are collected, algorithms to continuously incorporate the updated knowledge can play an essential role in this process. In regard to the continuous learning of the Bayesian network’s structure, the current solutions are based on its structural refinement or adaptation. Recent researchers aim to reduce complexity and memory usage, allowing to solve complex and large-scale practical problems. This study aims to identify and evaluate solutions for the continuous learning of the Bayesian network’s structures, as well as to outline related future research directions. Our attention remains on the structures because the accurate parameters are completely useless if the structure is not representative.
Part of the book: Bayesian Networks - Advances and Novel Applications
A major challenge in constructing a Bayesian network (BN) is defining the node probability tables (NPT), which can be learned from data or elicited from domain experts. In practice, it is common not to have enough data for learning, and elicitation from experts is the only option. However, the complexity of defining NPT grows exponentially, making their elicitation process costly and error-prone. In this research, we conducted an exploratory study through a literature review that identified the main issues related to the task of probability elicitation and solutions to construct large-scale NPT while reducing the exposure to these issues. In this chapter, we present in detail three semiautomatic methods that reduce the burden for experts. We discuss the benefits and drawbacks of these methods, and present directions on how to improve them.
Part of the book: Enhanced Expert Systems