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

Author

Ginestet, P. ; Blanc, J. ; Denoeux, Thierry

Volume

2

fYear

2002

fDate

8-11 July 2002

Firstpage

1613

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.

Keywords

belief networks; case-based reasoning; chemical engineering computing; pattern classification; prediction theory; water treatment; Bayesian networks; belief function; case-based approach; chemical oxygen demand solubility prediction; classification; expert knowledge; information sources; modelling; partial expert knowledge; performance criterion optimization; prediction; small databases; statistical data; tuning mechanism; waste-water treatment; Bayesian methods; Chemicals; Computer aided software engineering; Databases; Information resources; Optimization methods; Predictive models; Uncertainty; Wastewater treatment;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion, 2002. Proceedings of the Fifth International Conference on

Conference_Location

Annapolis, MD, USA

Print_ISBN

0-9721844-1-4

Type

conf

DOI

10.1109/ICIF.2002.1021011

Filename

1021011