Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2653
Title: Quasi-supervised learning for biomedical data analysis
Authors: Karaçalı, Bilge
Keywords: Data flow analysis
Abnormality detection
Biomedical data analysis
Electroencephalography
Support vector machines
Publisher: Elsevier Ltd.
Source: Karaçalı, B. (2010). Quasi-supervised learning for biomedical data analysis. Pattern Recognition, 43(10), 3674-3682. doi:10.1016/j.patcog.2010.04.024
Abstract: We present a novel formulation for pattern recognition in biomedical data. We adopt a binary recognition scenario where a control dataset contains samples of one class only, while a mixed dataset contains an unlabeled collection of samples from both classes. The mixed dataset samples that belong to the second class are identified by estimating posterior probabilities of samples for being in the control or the mixed datasets. Experiments on synthetic data established a better detection performance against possible alternatives. The fitness of the method in biomedical data analysis was further demonstrated on real multi-color flow cytometry and multi-channel electroencephalography data. © 2010 Elsevier Ltd. All rights reserved.
URI: http://doi.org/10.1016/j.patcog.2010.04.024
http://hdl.handle.net/11147/2653
ISSN: 0031-3203
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File Description SizeFormat 
2653.pdfMakale569.09 kBAdobe PDFThumbnail
View/Open
Show full item record



CORE Recommender

SCOPUSTM   
Citations

14
checked on Mar 22, 2024

WEB OF SCIENCETM
Citations

12
checked on Mar 27, 2024

Page view(s)

288
checked on Mar 25, 2024

Download(s)

304
checked on Mar 25, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.