Iterative, Probabilistic Classification Using Uncertain Information

Abstract

The authors have constructed an iterative, probabilistic reasoning architecture for classification problems. A number of assumptions of conditional independence have been employed in this architecture to derive two iterative updating methods, S and D. A Bayesian network was constructed and the results compared with the iterative methods. Method S and the network are both insensitive to the order of evidence, but do not produce the same results. Further investigation of the nature of these differences is warranted. It is suggested that additional information carried in the network may allow uncertain evidence to be used more effectively than in the iterative methods