Abstract
Several rule induction schemes generate hypotheses in the form of unordered rule sets. One important problem that has to be addressed when classifying examples with such hypotheses is how to deal with overlapping rules that predict different classes. Previous approaches to this problem calculate class probabilities based on the union of examples covered by the overlapping rules (as in CN2) or assumes rule independence (using naive Bayes). It is demonstrated that a significant improvement in accuracy can be obtained if class probabilities are calculated based on the intersection of the overlapping rules, or in case of an empty intersection, based on as few intersecting regions as possible.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Henrik Boström. Virtual Predict User Manual. Virtual Genetics Laboratory, 2001.
P. Clark and R. Boswell. Rule induction with CN2: Some recent improvements. In Proc. Fifth European Working Session on Learning, pages 151–163, Berlin, 1991. Springer.
P. Clark and T. Niblett. The cn2 induction algorithm. Machine Learning, 3, 261–283, 1989.
J.R. Quinlan. Induction of decision trees. Machine Learning, 1, 81–106, 1986.
R. Rivest. Learning decision lists. Machine Learning, 2(3), 229–246, 1987.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lindgren, T., Boström, H. (2002). Classification with Intersecting Rules. In: Cesa-Bianchi, N., Numao, M., Reischuk, R. (eds) Algorithmic Learning Theory. ALT 2002. Lecture Notes in Computer Science(), vol 2533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36169-3_31
Download citation
DOI: https://doi.org/10.1007/3-540-36169-3_31
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00170-6
Online ISBN: 978-3-540-36169-5
eBook Packages: Springer Book Archive