Using a permutation test for attribute selection in decision trees

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dc.contributor.authorFrank, Eibe
dc.contributor.authorWitten, Ian H.
dc.coverage.spatialConference held at Shavlik, Judeen_NZ
dc.date.accessioned2008-12-01T03:54:30Z
dc.date.available2008-12-01T03:54:30Z
dc.date.issued1998
dc.description.abstractMost techniques for attribute selection in decision trees are biased towards attributes with many values, and several ad hoc solutions to this problem have appeared in the machine learning literature. Statistical tests for the existence of an association with a prespecified significance level provide a well-founded basis for addressing the problem. However, many statistical tests are computed from a chi-squared distribution, which is only a valid approximation to the actural distribution in the large-sample case-and this patently does not hold near the leaves of a decision tree. An exception is the class of permutation tests. We describe how permutation tests can be applied to this problem. We choose one such test for further exploration, and give a novel two-stage method for applying it to select attributes in a decision tree. Results on practical datasets compare favourably with other methods that also adopt a pre-pruning strategy.en_US
dc.format.mimetypeapplication/pdf
dc.identifier.citationFrank, E. & Witten, I.H.(1998). Using a permutation test for attribute selection in decision trees. In Proceeding of 15th International Conference on Machine Learning, Madison, Wisconsin(pp.152-160). San Francisco: Morgan Kaufmann Publishers.en_US
dc.identifier.urihttps://hdl.handle.net/10289/1506
dc.language.isoen
dc.publisherMorgan Kaufmann Publishersen_US
dc.relation.isPartOfICML'98 15th International Conference on Machine Learningen_NZ
dc.relation.urihttp://www.cs.wisc.edu/en_US
dc.rightsThis article has been published in Proceeding of 15th International Conference on Machine Learning, Madison, Wisconsin. ©1998 Morgan Kaufmann.en_US
dc.subjectcomputer scienceen_US
dc.subjectattribute selectionen_US
dc.subjectdecision treeen_US
dc.subjectMachine learning
dc.titleUsing a permutation test for attribute selection in decision treesen_US
dc.typeConference Contributionen_US
pubs.begin-page152en_NZ
pubs.elements-id24247
pubs.end-page160en_NZ
pubs.place-of-publicationSan Franciscoen_NZ
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