Fusion of expert knowledge with data using belief functions: a case study in waste-water treatment

Abstract

This paper presents a methodology for combining expert knowledge with information from statistical data, in classification and prediction problems. The method is based on (1) a case-based approach allowing to predict a quantity of interest from past cases in the form of a belief function, (2) Bayesian networks for modelling expert knowledge and (3) a tuning mechanism allowing to optimally discount information sources by optimizing a performance criterion. This methodology is applied to the prediction of chemical oxygen demand solubility in waste-water The approach is expected to be useful in situations where both small databases and partial expert knowledge are available.