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.


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    Title :

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


    Contributors:
    Populaire, S. (author) / Ginestet, P. (author) / Blanc, J. (author) / Denoeux, T. (author)


    Publication date :

    2002-01-01


    Size :

    358600 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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