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dc.contributor.authorTing, Kai Ming
dc.contributor.authorZheng, Zijian
dc.date.accessioned2008-10-20T00:37:23Z
dc.date.available2008-10-20T00:37:23Z
dc.date.issued1998-01
dc.identifier.citationTing, K.M. & Zheng, Z. (1998). Boosting trees for cost-sensitive classifications. (Working paper 98/1). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1046
dc.description.abstractThis paper explores two boosting techniques for cost-sensitive tree classification in the situation where misclassification costs change very often. Ideally, one would like to have only one induction, and use the induced model for different misclassification costs. Thus, it demands robustness of the induced model against cost changes. Combining multiple trees gives robust predictions against this change. We demonstrate that ordinary boosting combined with the minimum expected cost criterion to select the prediction class is a good solution under this situation. We also introduce a variant of the ordinary boosting procedure which utilizes the cost information during training. We show that the proposed technique performs better than the ordinary boosting in terms of misclassification cost. However, this technique requires to induce a set of new trees every time the cost changes. Our empirical investigation also reveals some interesting behavior of boosting decision trees for cost-sensitive classification.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.subjectinductive learningen_US
dc.subjectboostingen_US
dc.subjectcost-sensitive classificationen_US
dc.subjectdecision-tree learningen_US
dc.subjectmachine learningen_US
dc.titleBoosting trees for cost-sensitive classificationsen_US
dc.typeWorking Paperen_US
uow.relation.series98/1
pubs.elements-id54740
pubs.place-of-publicationHamiltonen_NZ


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