Continuous Learning of the Structure of Bayesian Networks: A Mapping Study

Luiz Antonio Silva, Joao Nunes, Mirko Perkusich, Kyller Gorgonio, Hyggo Almeida and Angelo Perkusich

A chapter in Bayesian Networks - Advances and Novel Applications from IntechOpen

Abstract: 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.

Keywords: Bayesian network; structure learning; continuous learning; structural adaptation; structural refinement (search for similar items in EconPapers)
JEL-codes: C60 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ito:pchaps:160864

DOI: 10.5772/intechopen.80064

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