Test-cost sensitive classification based on conditioned loss functions

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dc.citation.epage558en_US
dc.citation.spage551en_US
dc.contributor.authorCebe, Müminen_US
dc.contributor.authorGündüz-Demir, Çiğdemen_US
dc.coverage.spatialWarsaw, Poland
dc.date.accessioned2016-02-08T11:42:11Z
dc.date.available2016-02-08T11:42:11Z
dc.date.issued2007-09en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionDate of Conference: 17 - 21 September, 2007
dc.descriptionConference name: ECML '07 Proceedings of the 18th European conference on Machine Learning
dc.description.abstractWe report a novel approach for designing test-cost sensitive classifiers that consider the misclassification cost together with the cost of feature extraction utilizing the consistency behavior for the first time. In this approach, we propose to use a new Bayesian decision theoretical framework in which the loss is conditioned with the current decision and the expected decisions after additional features are extracted as well as the consistency among the current and expected decisions. This approach allows us to force the feature extraction for samples for which the current and expected decisions are inconsistent. On the other hand, it forces not to extract any features in the case of consistency, leading to less costly but equally accurate decisions. In this work, we apply this approach to a medical diagnosis problem and demonstrate that it reduces the overall feature extraction cost up to 47.61 percent without decreasing the accuracy. © Springer-Verlag Berlin Heidelberg 2007.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T11:42:11Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2007en
dc.identifier.doi10.1007/978-3-540-74958-5_52
dc.identifier.urihttp://hdl.handle.net/11693/27022
dc.language.isoEnglishen_US
dc.publisherSpringer
dc.relation.isversionofhttps://doi.org/10.1007/978-3-540-74958-5_52
dc.source.titleECML '07 Proceedings of the 18th European conference on Machine Learningen_US
dc.subjectBayesian networksen_US
dc.subjectDecision theoryen_US
dc.subjectFeature extractionen_US
dc.subjectProblem solvingen_US
dc.subjectSensitivity analysisen_US
dc.subjectBayesian decision theoretical frameworken_US
dc.subjectConditioned loss functionsen_US
dc.subjectTest-cost sensitive classificationen_US
dc.subjectClassification (of information)en_US
dc.titleTest-cost sensitive classification based on conditioned loss functionsen_US
dc.typeConference Paperen_US

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