Improving the Applicability of Bayesian Networks through Production Rules
Raissa Da Silva, Mirko Perkusich, Renata Saraiva, Arthur Freire, Hyggo Oliveira de Almeida, Ângelo Perkusich · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · 2016
One of the key challenges in constructing a Bayesian network BN is defining the node probability tables (NPT).For large-scale BN, learning NPT through domain experts knowledge elicitation is unfeasible.Previous works proposed solutions to this problem using the concept of ranked nodes; however, they have limited modeling capabilities or rely on BN experts to apply them, reducing their applicability.In this paper, we present an expert system based on production rules to define NPTs with the purpose of enabling the definition of NPTs by experts with no ranked nodes-specific knowledge.To create the rules, we elicited data from an expert in ranked nodes.To validate our approach, we executed an experiment with a BN already published in the literature to verify if, with our approach, a practitioner can achieve the same or better configuration for the NPTs.We used the Brier score to assess the NPTs accuracy and evaluated the results with the Wilcoxon test.All the Wilcoxon tests executed rejected the null hypotheses that stated that the Brier scores for the original NPTs method were the same as the new NPTs.By using our solution, a practitioner can accurately define NPTs without understanding the concept of ranked nodes.