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Using Background Knowledge to Construct Bayesian Classifiers for Data-Poor Domains (2004)
BibTeX
@MISC{Gerven04usingbackground,
author = {Marcel Van Gerven and Peter Lucas},
title = {Using Background Knowledge to Construct Bayesian Classifiers for Data-Poor Domains},
year = {2004}
}
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Abstract
The development of Bayesian classifiers is frequently accomplished by means of algorithms which are highly data-driven. Often, however, sufficient data are not available, which may be compensated for by eliciting background knowledge from experts. This paper explores the trade-offs between modelling using background knowledge from domain experts and machine learning using a small clinical dataset in the context of Bayesian classifiers. We utilized background knowledge to improve Bayesian classifier performance, both in terms of classification accuracy and in terms of modelling the structure of the underlying joint probability distribution. Relative differences between models of differing structural complexity, which were learnt using varying amounts of background knowledge, are explored. It is shown that the use of partial background knowledge may significantly improve the quality of the resulting classifiers. 1
Keyphrases
background knowledge data-poor domain construct bayesian classifier bayesian classifier partial background knowledge classification accuracy relative difference bayesian classifier performance machine learning utilized background knowledge small clinical dataset sufficient data underlying joint probability distribution domain expert structural complexity
