Fuzzy Method for Online Learning of Bayesian Network Parameters

In learning problems, there are situations where training data is not fully available at the learning time. They are incrementally generated by time, defining a type of domain called online that has among its characteristics the possibility of data failure or even missing data. In Bayesian networks, learning is divided into two categories: structure (related to the graph of conditional relations) and parameters (related to the strength of conditional relations). In this work we present an online parameter learning method that quickly adapts to changes in the environment aiming not only the reproduction of the probability distribution (generative learning) but also the increase of accuracy in the network (discriminatory learning). Our approach is compared with the Adaptative Voting EM method considering two simulation conditions: when distributions are unknown and when distributions undergo abrupt changes. The proposed method achieves good results in both situations by adjusting to environment changes more quickly and by simplifying the parameterization of the traditional approach.