Competing Fusion for Bayesian Applications
Michael Abramovici, Manuel Neubach, Madjid Fathi, Alexander Holland · 2008
In this paper we address and discuss the problem of learning graphical models like Bayesian networks using structure learning algorithms. We present a new parameterized structure learning approach. A competing fusion mechanism to aggregate expert knowledge stored in distributed knowledge bases or probability distributions is also described. Experimental results of a medical case study show that our approach can improve the quality of the learned graphical model.