Constructing belief networks from realistic data

Abstract

In this paper, we report our investigation on the behavior of some Bayesian belief network learning methods, when applied to realistic case data that do not accurately reflect the probability distribution of the underlying domain. The investigation focuses on two types of such data of practical interest, namely data excluding null or normal cases and data contaminated by noise, and their effects on the performance of two learning methods: the K2 algorithm, a representative method of Bayesian approach, and the extended Hebbian learning (EHL) method, representing those of neural network approach. Both analytical and experimental results show that when null cases are removed from the case database, the EHL method is able to accurately construct the underlying causal structure; whereas K2 algorithm may fail to properly handle root nodes. On the other hand, the EHL method is noise sensitive while K2 has certain inherent noise-resistance capability. Suggestions to extend both K2 and EHL to better cope with these problems are also made, and their effectiveness tested through a systematic computer simulation experiment.