LiU Electronic Press
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Author:
Kleiner, Alexander (Staffordshire University)
Sharp, Bernadette (Staffordshire University)
Title:
A New Algorithm for Learning Bayesian Classifiers from Data
Publication type:
Conference paper (Refereed)
Language:
English
In:
Artificial Intelligence and Soft Computing
Pages:
191-197
Year of publ.:
2000
URI:
urn:nbn:se:liu:diva-72562
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72562
Subject category:
Computer Systems
Project:
Artificial Intelligence & Integrated Computer Systems
Abstract(en) :

We introduce a new algorithm for the induction of classifiers from data, based on Bayesian networks. Basically this problem has already been examined from two perspectives: first, the induction of classifiers by learning algorithms for Bayesian networks, second, the induction of classifiers based on the naive Bayesian classifier. Our approach is located between these two perspectives; it eliminates the disadvantages of both while exploiting their advantages. In contrast to recently appeared refinements of the naive Bayes classifier, which captures single correlations in the data, we have developed an approach which captures multiple correlations and furthermore does a trade-off between complexity and accuracy. In this paper we evaluate the implementation of our approach with data sets from the machine learning repository and data sets artificially generated by Bayesian networks.

Available from:
2011-11-28
Created:
2011-11-28
Last updated:
2011-12-06
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