A disagreement based co-active learning method for sleep stage classification

Ayşe Betül Yüce; Yusuf Yaslan

doi:10.1109/IWSSIP.2016.7502740

A disagreement based co-active learning method for sleep stage classification

Ayşe Betül Yüce
, Yusuf Yaslan

Bilgisayar Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

3 Atıf (Scopus)

Özet

In this paper a co-active learning framework that uses two feature views for sleep stage classification is proposed. At the beginning of active learning, classifiers are trained on two separate feature views namely Empirical Mode Decomposition (EMD) and frequency domain based energy features which are obtained from different patients' labelled electroencephalography (EEG) data. These classifiers are updated on new patients' unlabeled data using a disagreement based co-active learning framework. Experimental results obtained on Sleep-EDF database show that the proposed co-active learning method outperforms single view based active learning and the proposed method boosts the classification accuracy to approximately 84.85% on average.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	IWSSIP 2016 - Proceedings of the 23rd International Conference on Systems, Signals and Image Processing
Editörler	Renata Rybarova, Gregor Rozinaj, Ivan Minarik, Peter Truchly
Yayınlayan	IEEE Computer Society
ISBN (Elektronik)	9781467395557
DOI'lar	https://doi.org/10.1109/IWSSIP.2016.7502740
Yayın durumu	Yayınlandı - 30 Haz 2016
Etkinlik	23rd International Conference on Systems, Signals and Image Processing, IWSSIP 2016 - Bratislava, Slovakia Süre: 23 May 2016 → 25 May 2016

Yayın serisi

Adı	International Conference on Systems, Signals, and Image Processing
Hacim	2016-June
ISSN (Basılı)	2157-8672
ISSN (Elektronik)	2157-8702

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	23rd International Conference on Systems, Signals and Image Processing, IWSSIP 2016
Ülke/Bölge	Slovakia
Şehir	Bratislava
Periyot	23/05/16 → 25/05/16

Bibliyografik not

Publisher Copyright:
© 2016 IEEE.

Belgeye Erişim

10.1109/IWSSIP.2016.7502740

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Betül Yüce, A., & Yaslan, Y. (2016). A disagreement based co-active learning method for sleep stage classification. In R. Rybarova, G. Rozinaj, I. Minarik, & P. Truchly (Eds.), IWSSIP 2016 - Proceedings of the 23rd International Conference on Systems, Signals and Image Processing Makale 7502740 (International Conference on Systems, Signals, and Image Processing; Hac. 2016-June). IEEE Computer Society. https://doi.org/10.1109/IWSSIP.2016.7502740

Betül Yüce, Ayşe ; Yaslan, Yusuf. / A disagreement based co-active learning method for sleep stage classification. IWSSIP 2016 - Proceedings of the 23rd International Conference on Systems, Signals and Image Processing. editör / Renata Rybarova ; Gregor Rozinaj ; Ivan Minarik ; Peter Truchly. IEEE Computer Society, 2016. (International Conference on Systems, Signals, and Image Processing).

@inproceedings{85b69fcc2631438d84ca0fcc1e3f631b,

title = "A disagreement based co-active learning method for sleep stage classification",

abstract = "In this paper a co-active learning framework that uses two feature views for sleep stage classification is proposed. At the beginning of active learning, classifiers are trained on two separate feature views namely Empirical Mode Decomposition (EMD) and frequency domain based energy features which are obtained from different patients' labelled electroencephalography (EEG) data. These classifiers are updated on new patients' unlabeled data using a disagreement based co-active learning framework. Experimental results obtained on Sleep-EDF database show that the proposed co-active learning method outperforms single view based active learning and the proposed method boosts the classification accuracy to approximately 84.85\% on average.",

keywords = "Active Learning, Empirical Mode Decomposition, Fast Fourier Transform, Sleep Stage Classification",

author = "\{Bet{\"u}l Y{\"u}ce\}, Ay{\c s}e and Yusuf Yaslan",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 23rd International Conference on Systems, Signals and Image Processing, IWSSIP 2016 ; Conference date: 23-05-2016 Through 25-05-2016",

year = "2016",

month = jun,

day = "30",

doi = "10.1109/IWSSIP.2016.7502740",

language = "English",

series = "International Conference on Systems, Signals, and Image Processing",

publisher = "IEEE Computer Society",

editor = "Renata Rybarova and Gregor Rozinaj and Ivan Minarik and Peter Truchly",

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Betül Yüce, A & Yaslan, Y 2016, A disagreement based co-active learning method for sleep stage classification. in R Rybarova, G Rozinaj, I Minarik & P Truchly (eds), IWSSIP 2016 - Proceedings of the 23rd International Conference on Systems, Signals and Image Processing., 7502740, International Conference on Systems, Signals, and Image Processing, hac. 2016-June, IEEE Computer Society, 23rd International Conference on Systems, Signals and Image Processing, IWSSIP 2016, Bratislava, Slovakia, 23/05/16. https://doi.org/10.1109/IWSSIP.2016.7502740

A disagreement based co-active learning method for sleep stage classification. / Betül Yüce, Ayşe; Yaslan, Yusuf.
IWSSIP 2016 - Proceedings of the 23rd International Conference on Systems, Signals and Image Processing. ed. / Renata Rybarova; Gregor Rozinaj; Ivan Minarik; Peter Truchly. IEEE Computer Society, 2016. 7502740 (International Conference on Systems, Signals, and Image Processing; Hac. 2016-June).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - A disagreement based co-active learning method for sleep stage classification

AU - Betül Yüce, Ayşe

AU - Yaslan, Yusuf

PY - 2016/6/30

Y1 - 2016/6/30

N2 - In this paper a co-active learning framework that uses two feature views for sleep stage classification is proposed. At the beginning of active learning, classifiers are trained on two separate feature views namely Empirical Mode Decomposition (EMD) and frequency domain based energy features which are obtained from different patients' labelled electroencephalography (EEG) data. These classifiers are updated on new patients' unlabeled data using a disagreement based co-active learning framework. Experimental results obtained on Sleep-EDF database show that the proposed co-active learning method outperforms single view based active learning and the proposed method boosts the classification accuracy to approximately 84.85% on average.

AB - In this paper a co-active learning framework that uses two feature views for sleep stage classification is proposed. At the beginning of active learning, classifiers are trained on two separate feature views namely Empirical Mode Decomposition (EMD) and frequency domain based energy features which are obtained from different patients' labelled electroencephalography (EEG) data. These classifiers are updated on new patients' unlabeled data using a disagreement based co-active learning framework. Experimental results obtained on Sleep-EDF database show that the proposed co-active learning method outperforms single view based active learning and the proposed method boosts the classification accuracy to approximately 84.85% on average.

KW - Active Learning

KW - Empirical Mode Decomposition

KW - Fast Fourier Transform

KW - Sleep Stage Classification

UR - https://www.scopus.com/pages/publications/84978807226

U2 - 10.1109/IWSSIP.2016.7502740

DO - 10.1109/IWSSIP.2016.7502740

M3 - Conference contribution

AN - SCOPUS:84978807226

T3 - International Conference on Systems, Signals, and Image Processing

BT - IWSSIP 2016 - Proceedings of the 23rd International Conference on Systems, Signals and Image Processing

A2 - Rybarova, Renata

A2 - Rozinaj, Gregor

A2 - Minarik, Ivan

A2 - Truchly, Peter

PB - IEEE Computer Society

T2 - 23rd International Conference on Systems, Signals and Image Processing, IWSSIP 2016

Y2 - 23 May 2016 through 25 May 2016

ER -

Betül Yüce A, Yaslan Y. A disagreement based co-active learning method for sleep stage classification. In Rybarova R, Rozinaj G, Minarik I, Truchly P, editörler, IWSSIP 2016 - Proceedings of the 23rd International Conference on Systems, Signals and Image Processing. IEEE Computer Society. 2016. 7502740. (International Conference on Systems, Signals, and Image Processing). doi: 10.1109/IWSSIP.2016.7502740

A disagreement based co-active learning method for sleep stage classification

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Bibliyografik not

Belgeye Erişim

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