Building a Bayesian Network Model Based on the Combination of Structure Learning Algorithms and Weighting Expert Opinions Scheme

Abstract

Bayesian networks (BNs) is probabilistic graphical models that are widely used for building expert systems in several application domains. In the context of expert systems, either probabilistic or heuristic, the development of explanation facilities is important for three main reasons. First, the construction of those systems with the help of human experts is a difficult and time consuming task, and prone to errors and omissions. A Bayesian network tool can help the knowledge engineers and experts who are taking part in the project to debug the system when it does not yield the expected results and even before a malfunction occurs. Second, human beings are reluctant to accept the advice that is offered by a machine if they are not able to understand how the system arrived at those recommendations. Third, an expert system that is used as an intelligent tutor must be able to communicate to the apprentice the knowledge it contains, the way in which the knowledge has been applied to arrive at a conclusion, and what would have happened if the user had introduced different pieces of evidence (what-if reasoning). One of the most difficult obstacles in the practical application of probabilistic methods is the effort that is required for model building and, in particular, for quantifying graphical models with numerical probabilities. The construction of Bayesian Networks (BNs) with the help of human experts is a difficult and time consuming task, which is prone to errors and omissions especially when the problems are very complicated or there are numerous variables involved. Learning the structure of a BN model and causal relations from a dataset or database is important for extensive BNs analysis. In general, the causal structure and the numerical parameters of a BN can be obtained using two distinct approaches. First, they can be obtained from an expert. Second, they can also be learned from a data set. The main drawback of the first approach is that sometimes there is not enough causal knowledge to establish the structure of the network model with certainty and estimation of probabilities required for a typical application is a time-consuming task because of the number of parameters required (typically hundreds or even thousands of values). Thus, the second approach can initially help human experts or a group of experts build a BN model and they can make it applicable at a later time. In practice, some combination of these two approaches is typically used.