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
Issue Date: Oct-2010
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

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