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dc.contributor.authorHall, Mark A.
dc.contributor.authorHolmes, Geoffrey
dc.date.accessioned2008-10-10T03:51:09Z
dc.date.available2008-10-10T03:51:09Z
dc.date.issued2002-04
dc.identifier.citationHolmes, G., Pfahringer, B., Kirkby, R., Frank, E. & Hall, M. (2002). Benchmarking attribute selection techniques for discrete class data mining. (Working paper 02/02). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1013
dc.description.abstractData engineering is generally considered to be a central issue in the development of data mining applications. The success of many learning schemes, in their attempts to construct models of data, hinges on the reliable identification of a small set of highly predictive attributes. The inclusion of irrelevant, redundant and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. Attribute selection generally involves a combination of search and attribute utility estimation plus evaluation with respect to specific learning schemes. This leads to a large number of possible permutation and has led to a situation where very few benchmark studies have been conducted. This paper presents a benchmark comparison of several attribute selection methods for supervised classification. All the methods produce an attribute ranking, a useful devise for isolating the individual merit of an attribute. Attribute selection is achieved by cross-validating the attribute rankings with respect to a classification learner to find the best attributes. Results are reported for a selection of standard data sets and two diverse learning schemes C4.5 and naïve Bayes.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.subjectMachine learning
dc.titleBenchmarking attribute selection techniques for discrete class data miningen_US
dc.typeWorking Paperen_US
uow.relation.series02/02
pubs.elements-id52145
pubs.place-of-publicationHamilton, New Zealanden_NZ


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