Relational random forests based on random relational rules

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dc.contributor.authorAnderson, Grant
dc.contributor.authorPfahringer, Bernhard
dc.coverage.spatialConference held at Pasadena, California, USAen_NZ
dc.date.accessioned2010-12-05T20:07:19Z
dc.date.available2010-12-05T20:07:19Z
dc.date.issued2009
dc.description.abstractRandom Forests have been shown to perform very well in propositional learning. FORF is an upgrade of Random Forests for relational data. In this paper we investigate shortcomings of FORF and propose an alternative algorithm, R⁴F, for generating Random Forests over relational data. R⁴F employs randomly generated relational rules as fully self-contained Boolean tests inside each node in a tree and thus can be viewed as an instance of dynamic propositionalization. The implementation of R⁴F allows for the simultaneous or parallel growth of all the branches of all the trees in the ensemble in an efficient shared, but still single-threaded way. Experiments favorably compare R⁴F to both FORF and the combination of static propositionalization together with standard Random Forests. Various strategies for tree initialization and splitting of nodes, as well as resulting ensemble size, diversity, and computational complexity of R⁴F are also investigated.en_NZ
dc.format.mimetypeapplication/pdf
dc.identifier.citationAnderson, G. & Pfahringer, B. (2009). Relational random forests based on random relational rules. In H. Kitano (Ed.), IJCAI'09 Proceedings of the 21st international joint conference on Artificial intelligence (pp. 986-991), San Francisco, USA: Morgan Kaufmann Publishers Inc.en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/4842
dc.language.isoen
dc.publisherMorgan Kaufmann Publishers Inc. San Francisco, CA, USAen_NZ
dc.relation.isPartOfProc International Joint Conference on Artificial Intelligenceen_NZ
dc.relation.urihttp://ijcai.org/papers09/Papers/IJCAI09-167.pdfen_NZ
dc.rightsThis article has been published in IJCAI'09 Proceedings of the 21st international joint conference on Artificial intelligence. Use with permission.en_NZ
dc.source21st Internation Joint Conference on Artifical Intelligence (IJCAI-09)en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectRandom Foresten_NZ
dc.subjectrandom relational ruleen_NZ
dc.subjectMachine learning
dc.titleRelational random forests based on random relational rulesen_NZ
dc.typeConference Contributionen_NZ
pubs.begin-page986en_NZ
pubs.elements-id19168
pubs.end-page991en_NZ
pubs.finish-date2009-07-17en_NZ
pubs.start-date2009-07-11en_NZ
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