Improving Bayesian learning using public knowledge

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TypeBook Chapter
Proceedings titleAdvances in Artificial Intelligence : 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings
Series titleLecture Notes In Computer Science; Volume 6085
Conference23rd Canadian Conference on Artificial Intelligence (Canadian AI 2010), May 31-June 2, 2010, Ottawa, Ontario, Canada
Pages348351; # of pages: 4
SubjectBayesian Networks; Public Knowledge; Classification
AbstractBoth intensional and extensional background knowledge have previously been used in inductive problems to complement the training set used for a task. In this research, we propose to explore the usefulness, for inductive learning, of a new kind of intensional background knowledge: the inter-relationships or conditional probability distributions between subsets of attributes. Such information could be mined from publicly available knowledge sources but including only some of the attributes involved in the inductive task at hand. The purpose of our work is to show how this information can be useful in inductive tasks, and under what circumstances. We will consider injection of background knowledge into Bayesian Networks and explore its effectiveness on training sets of different sizes. We show that this additional knowledge not only improves the estimate of classification accuracy, it also reduces the variance in the accuracy of the model.
Publication date
PublisherSpringer Berlin Heidelberg
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology
Peer reviewedYes
NPARC number15336796
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Record identifierc4fb02d6-447c-468d-b489-46899e408328
Record created2010-06-10
Record modified2016-06-22
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