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

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) Bayesi...

Full description

Saved in:
Bibliographic Details
Main Authors: Populaire, S., Ginestet, P., Blanc, J., Denoeux, T.
Format: Conference Proceeding
Language:English
Subjects:
Bayesian methods
Chemicals
Computer aided software engineering
Information resources
Optimization methods
Predictive models
Uncertainty
Wastewater treatment
Citations: Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
DOI:10.1109/ICIF.2002.1021011