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dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorAnderson, Grant
dc.date.accessioned2008-11-25T01:47:18Z
dc.date.available2008-11-25T01:47:18Z
dc.date.issued2006
dc.identifier.citationAnderson, G. & Pfahringer, B. (2006). Random Relational Rules. In Proceedings of the 16th International Conference on Inductive Logic Programming, Santiago de Compostela, Spain, August 24-27. Santiago de Compostela, Spain: University of Coruna.en_US
dc.identifier.isbn8497492064
dc.identifier.urihttps://hdl.handle.net/10289/1458
dc.description.abstractExhaustive search in relational learning is generally infeasible, therefore some form of heuristic search is usually employed, such as in FOIL[1]. On the other hand, so-called stochastic discrimination provides a framework for combining arbitrary numbers of weak classifiers (in this case randomly generated relational rules) in a way where accuracy improves with additional rules, even after maximal accuracy on the training data has been reached. [2] The weak classifiers must have a slightly higher probability of covering instances of their target class than of other classes. As the rules are also independent and identically distributed, the Central Limit theorem applies and as the number of weak classifiers/rules grows, coverages for different classes resemble well-separated normal distributions. Stochastic discrimination is closely related to other ensemble methods like Bagging, Boosting, or Random forests, all of which have been tried in relational learning [3, 4, 5].en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.urihttp://www.dc.fi.udc.es/ilp06/accepted.htmlen_US
dc.subjectcomputer scienceen_US
dc.subjectrandom relational rulesen_US
dc.titleRandom Relational Rulesen_US
dc.typeConference Contributionen_US


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