On the comparison of classifiers' performance in emotion classification: Critiques and Suggestions

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dc.authorid0000-0002-2126-8757
dc.contributor.authorAltun, Halis
dc.contributor.authorPolat, Goekhan
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2008
dc.departmentNiğde ÖHÜ
dc.descriptionIEEE 16th Signal Processing and Communications Applications Conference -- APR 20-22, 2008 -- Aydin, TURKEY
dc.description.abstractIn literature there is a huge body of references available which compare various classifiers in a particular application. However, the reliability of such a comparison is only valid if the model parameters, performance criteria and training environment are chosen in a fair framework, as successful application of a classifier is dependent on the those parameters. In this study we attempt to answer the questions below in a emotion detection framework, using classifiers such as KNN, SVM, RBF and MLP: Is the success of a classifier enough to make the claim that a classifier is "the best one" in a particular classification task? How is it possible to carry out a fair comparison between classifiers?
dc.description.sponsorshipIEEE
dc.identifier.endpage224
dc.identifier.isbn978-1-4244-1998-2
dc.identifier.scopus2-s2.0-56449118601
dc.identifier.scopusqualityN/A
dc.identifier.startpage221
dc.identifier.urihttps://hdl.handle.net/11480/5283
dc.identifier.wosWOS:000261359200056
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isotr
dc.publisherIEEE
dc.relation.ispartof2008 IEEE 16TH SIGNAL PROCESSING, COMMUNICATION AND APPLICATIONS CONFERENCE, VOLS 1 AND 2
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.titleOn the comparison of classifiers' performance in emotion classification: Critiques and Suggestions
dc.typeConference Object

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