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dc.contributor.authorFrank, Eibe
dc.contributor.authorWitten, Ian H.
dc.date.accessioned2008-10-20T00:46:18Z
dc.date.available2008-10-20T00:46:18Z
dc.date.issued1998-01
dc.identifier.citationFrank, E. & Witten, I. H. (1998). Generating accurate rule sets without global optimization. (Working paper 98/2). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1047
dc.description.abstractThe two dominant schemes for rule-learning, C4.5 and RIPPER, both operate in two stages. First they induce an initial rule set and then they refine it using a rather complex optimization stage that discards (C4.5) or adjusts (RIPPER) individual rules to make them work better together. In contrast, this paper shows how good rule sets can be learned one rule at a time, without any need for global optimization. We present an algorithm for inferring rules by repeatedly generating partial decision trees, thus combining the two major paradigms for rule generation-creating rules from decision trees and the separate-and-conquer rule-learning technique. The algorithm is straightforward and elegant: despite this, experiments on standard datasets show that it produces rule sets that are as accurate as and of similar size to those generated by C4.5, and more accurate than RIPPER’s. Moreover, it operates efficiently, and because it avoids postprocessing, does not suffer the extremely slow performance on pathological example sets for which the C4.5 method has been criticized.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherUniversity of Waikato, Department of Computer Scienceen_US
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectcomputer scienceen_US
dc.subjectrulesen_US
dc.subjectglobal optimizationen_US
dc.subjectpartial decision treesen_US
dc.subjectMachine learning
dc.titleGenerating accurate rule sets without global optimizationen_US
dc.typeWorking Paperen_US
uow.relation.series98/2


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