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dc.contributor.authorGurgen, Fikret
dc.contributor.authorSakar, C. Okan
dc.contributor.authorSeker, Huseyin
dc.contributor.authorAYDIN, Nizamettin
dc.contributor.authorFavorov, Oleg
dc.contributor.authorKursun, Olcay
dc.date.accessioned2021-03-05T14:46:09Z
dc.date.available2021-03-05T14:46:09Z
dc.identifier.citationSakar C. O. , Kursun O., Seker H., Gurgen F., AYDIN N., Favorov O., "Combining multiple views: Case studies on protein and arrhythmia features", ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, cilt.28, ss.174-180, 2014
dc.identifier.issn0952-1976
dc.identifier.othervv_1032021
dc.identifier.otherav_b8bbc025-2b23-4334-a206-6f6d018e8526
dc.identifier.urihttp://hdl.handle.net/20.500.12627/122911
dc.identifier.urihttps://doi.org/10.1016/j.engappai.2013.11.004
dc.description.abstractComputational annotation of protein functions and structures from sequence features, or prediction of certain diseases from gene expression levels are among important applications of computational biology. Developing methods capable of such predictions are not only important in terms of their biological and medical uses but also a very challenging task of pattern recognition due to high input dimensionality and small sample size. Ensemble and multi-view learning has gained popularity due to the rapid rise of such datasets (such as the protein and arrhythmia datasets used in this paper) with large numbers of variables. However, the classical ensemble approach does not take into account conditional interdependences among the views. In this paper, we present a two stage supervised multi-view learning technique called parallel interacting multi-view learning (PIML). In the first stage of PIML, similar to the ensemble method, the views are individually used by a predictor, and the class posterior probability estimates are obtained. In the second stage, each view is trained using its own features along with the class posterior probability estimates of the other views as the summary information of other views. This is a hybrid way of combining the views in which the views influence each other during training using the predictions of others interdependences. PIML is demonstrated and compared with the classical ensemble approach on three real datasets. (C) 2013 Elsevier Ltd. All rights reserved.
dc.language.isoeng
dc.subjectHarita Mühendisliği-Geomatik
dc.subjectBilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği
dc.subjectKontrol ve Sistem Mühendisliği
dc.subjectSinyal İşleme
dc.subjectBilgisayar Bilimleri
dc.subjectAlgoritmalar
dc.subjectMühendislik ve Teknoloji
dc.subjectMÜHENDİSLİK, MULTİDİSİPLİNER
dc.subjectMÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK
dc.subjectBilgisayar Bilimi
dc.subjectBİLGİSAYAR BİLİMİ, YAPAY ZEKA
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectMühendislik
dc.subjectOTOMASYON & KONTROL SİSTEMLERİ
dc.titleCombining multiple views: Case studies on protein and arrhythmia features
dc.typeMakale
dc.relation.journalENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
dc.contributor.departmentİstanbul Üniversitesi , ,
dc.identifier.volume28
dc.identifier.startpage174
dc.identifier.endpage180
dc.contributor.firstauthorID74462


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