Uncertainty in Artificial Intelligence
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Experiments Using Belief Functions and Weights of Evidence incorporating Statistical Data and Expert Opinions
Mary McLeish, P. Yao, M. Cecile, T. Stirtzinger
Abstract:
This paper presents some ideas and results of using uncertainty management methods in the presence of data in preference to other statistical and machine learning methods. A medical domain is used as a test-bed with data available from a large hospital database system which collects symptom and outcome information about patients. Data is often missing, of many variable types and sample sizes for particular outcomes is not large. Uncertainty management methods are useful for such domains and have the added advantage of allowing for expert modification of belief values originally obtained from data. Methodological considerations for using belief functions on statistical data are dealt with in some detail. Expert opinions are Incorporated at various levels of the project development and results are reported on an application to liver disease diagnosis. Recent results contrasting the use of weights of evidence and logistic regression on another medical domain are also presented.
Keywords:
Pages: 253-264
PS Link:
PDF Link: /papers/89/p253-mcleish.pdf
BibTex:
@INPROCEEDINGS{McLeish89,
AUTHOR = "Mary McLeish and P. Yao and M. Cecile and T. Stirtzinger",
TITLE = "Experiments Using Belief Functions and Weights of Evidence incorporating Statistical Data and Expert Opinions",
BOOKTITLE = "Proceedings of the Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-89)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1989",
PAGES = "253--264"
}


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