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dc.contributor.authorToraman, Çağrıen_US
dc.contributor.authorCan, Fazlıen_US
dc.coverage.spatialDubai, United Arab Emiratesen_US
dc.date.accessioned2016-02-08T12:15:18Z
dc.date.available2016-02-08T12:15:18Z
dc.date.issued2011en_US
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11693/28247
dc.descriptionConference name: 7th Asia Information Retrieval Societies Conference, AIRS 2011en_US
dc.descriptionDate of Conference: December 18-20, 2011en_US
dc.description.abstractEnsemble methods can improve the effectiveness in text categorization. Due to computation cost of ensemble approaches there is a need for pruning ensembles. In this work we study ensemble pruning based on data partitioning. We use a ranked-based pruning approach. For this purpose base classifiers are ranked and pruned according to their accuracies in a separate validation set. We employ four data partitioning methods with four machine learning categorization algorithms. We mainly aim to examine ensemble pruning in text categorization. We conduct experiments on two text collections: Reuters-21578 and BilCat-TRT. We show that we can prune 90% of ensemble members with almost no decrease in accuracy. We demonstrate that it is possible to increase accuracy of traditional ensembling with ensemble pruning. © 2011 Springer-Verlag Berlin Heidelberg.en_US
dc.language.isoEnglishen_US
dc.source.titleInformation Retrieval Technologyen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/978-3-642-25631-8_32en_US
dc.relation.isversionofhttps://doi.org/10.1007/978-3-642-25631-8en_US
dc.subjectData partitioningen_US
dc.subjectBase classifiersen_US
dc.subjectComputation costsen_US
dc.subjectData partitioningen_US
dc.subjectData-partitioning methoden_US
dc.subjectEnsemble membersen_US
dc.subjectEnsemble methodsen_US
dc.subjectEnsemble pruningen_US
dc.subjectReuters-21578en_US
dc.subjectText categorizationen_US
dc.subjectText collectionen_US
dc.subjectData handlingen_US
dc.subjectInfrared devicesen_US
dc.subjectText processingen_US
dc.subjectInformation retrievalen_US
dc.titleEnsemble pruning for text categorization based on data partitioningen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage352en_US
dc.citation.epage361en_US
dc.citation.volumeNumber7097en_US
dc.identifier.doi10.1007/978-3-642-25631-8_32en_US
dc.identifier.doi10.1007/978-3-642-25631-8en_US
dc.publisherSpringer, Berlin, Heidelbergen_US


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