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dc.contributor.advisorGüvenir, Altay
dc.contributor.authorİkizler, Nazlı
dc.date.accessioned2016-07-01T10:56:13Z
dc.date.available2016-07-01T10:56:13Z
dc.date.issued2002
dc.identifier.urihttp://hdl.handle.net/11693/29234
dc.descriptionCataloged from PDF version of article.en_US
dc.description.abstractFor a long time, classification algorithms have focused on minimizing the quantity of prediction errors by assuming that each possible error has identical consequences. However, in many real-world situations, this assumption is not convenient. For instance, in a medical diagnosis domain, misdiagnosing a sick patient as healthy is much more serious than its opposite. For this reason, there is a great need for new classification methods that can handle asymmetric cost and benefit constraints of classifications. In this thesis, we discuss cost-sensitive classification concepts and propose a new classification algorithm called Benefit Maximization with Feature Intervals (BMFI) that uses the feature projection based knowledge representation. In the framework of BMFI, we introduce five different voting methods that are shown to be effective over different domains. A number of generalization and pruning methodologies based on benefits of classification are implemented and experimented. Empirical evaluation of the methods has shown that BMFI exhibits promising performance results compared to recent wrapper cost-sensitive algorithms, despite the fact that classifier performance is highly dependent on the benefit constraints and class distributions in the domain. In order to evaluate costsensitive classification techniques, we describe a new metric, namely benefit accuracy which computes the relative accuracy of the total benefit obtained with respect to the maximum possible benefit achievable in the domain.en_US
dc.description.statementofresponsibilityİkizler, Nazlıen_US
dc.format.extentxiv, 109 leaves, tables, graphs, 30 cmen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectmachine learningen_US
dc.subjectclassificationen_US
dc.subjectcost-sensitivityen_US
dc.subjectbenefit maximizationen_US
dc.subjectfeature intervalsen_US
dc.subjectvotingen_US
dc.subjectpruningen_US
dc.subject.lccQ325.5 .I35 2002en_US
dc.subject.lcshMachine learning.en_US
dc.titleBenefit maximizing classification using feature intervalsen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidBILKUTUPB067736


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