New frameworks to boost feature selection algorithms in emotion detection for improved human-computer interaction

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dc.authorid0000-0002-2126-8757
dc.contributor.authorAltun, Halis
dc.contributor.authorPolat, Goekhan
dc.contributor.editorMele, F
dc.contributor.editorSantillo, S
dc.contributor.editorRamella, G
dc.contributor.editorVentriglia, F
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2007
dc.departmentNiğde ÖHÜ
dc.description2nd International Symposium on Brain, Vision and Artificial Intelligence -- OCT 10-12, 2007 -- Naples, ITALY
dc.description.abstractOne of the primary aims in human-computer interaction research is to develop an ability to recognize affective state of the user. Such ability is indispensable to have a more human-like nature in human-computer interaction. However, the researches in this direction are not mature and intensive efforts have only been witnessed recently. This work envisages the possibility of enhancing feature selection phase of emotion detection task to obtain robust parameters which will be determined from verbal information to achieve an improved affective human-computer interaction. As highly informative feature selection is believed to be a more critical factor than classifier itself, recent studies have increasingly focussed on determining features that contribute more to the classification problem. Two new frameworks for multi-class emotion detection problem are proposed in this paper, so as to boost the feature selection algorithms in a way that the selected features will be more informative in terms of class-separability. Evaluation of the selected final features is accomplished by multi-class classifiers. Results show that the proposed frameworks are successful in terms of attaining lower average cross-validation error.
dc.description.sponsorshipICIP CNR, IISF, EBSA, GIRPR, MARS Ctr, NEATEK SpA, PAN, SINS, Reg Campania
dc.description.sponsorshipTUBITAK [104E179]
dc.description.sponsorshipThis work has been sponsored by TUBITAK Project under the contract of 104E179. Corresponding author also would like to thank Prof. Dr. J Shawe-Taylor for his hospitality and guidance during the academic visit in the summer of 2006 granted by TUBITAK at University of Southampton and at University College of London.
dc.identifier.endpage+
dc.identifier.isbn978-3-540-75554-8
dc.identifier.issn0302-9743
dc.identifier.scopus2-s2.0-49949085938
dc.identifier.scopusqualityQ3
dc.identifier.startpage533
dc.identifier.urihttps://hdl.handle.net/11480/5431
dc.identifier.volume4729
dc.identifier.wosWOS:000250716000051
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherSPRINGER-VERLAG BERLIN
dc.relation.ispartofADVANCES IN BRAIN, VISION, AND ARTIFICIAL INTELLIGENCE, PROCEEDINGS
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjecthuman-computer interaction
dc.subjectemotion detection
dc.subjectaffective computing
dc.subjectpattern recognition
dc.titleNew frameworks to boost feature selection algorithms in emotion detection for improved human-computer interaction
dc.typeConference Object

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