Quasi-Supervised Learning for Biomedical Data Analysis
| dc.contributor.author | Karaçalı, Bilge | |
| dc.coverage.doi | 10.1016/j.patcog.2010.04.024 | |
| dc.date.accessioned | 2016-12-22T12:04:15Z | |
| dc.date.available | 2016-12-22T12:04:15Z | |
| dc.date.issued | 2010 | |
| dc.description.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. | en_US |
| dc.identifier.citation | Karaçalı, B. (2010). Quasi-supervised learning for biomedical data analysis. Pattern Recognition, 43(10), 3674-3682. doi:10.1016/j.patcog.2010.04.024 | en_US |
| dc.identifier.doi | 10.1016/j.patcog.2010.04.024 | |
| dc.identifier.issn | 0031-3203 | |
| dc.identifier.scopus | 2-s2.0-77953613179 | |
| dc.identifier.uri | http://doi.org/10.1016/j.patcog.2010.04.024 | |
| dc.identifier.uri | http://hdl.handle.net/11147/2653 | |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier Ltd. | en_US |
| dc.relation.ispartof | Pattern Recognition | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Data flow analysis | en_US |
| dc.subject | Abnormality detection | en_US |
| dc.subject | Biomedical data analysis | en_US |
| dc.subject | Electroencephalography | en_US |
| dc.subject | Support vector machines | en_US |
| dc.title | Quasi-Supervised Learning for Biomedical Data Analysis | en_US |
| dc.type | Article | en_US |
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| gdc.author.institutional | Karaçalı, Bilge | |
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| gdc.description.department | İzmir Institute of Technology. Electrical and Electronics Engineering | en_US |
| gdc.description.endpage | 3682 | en_US |
| gdc.description.issue | 10 | en_US |
| gdc.description.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | Q1 | |
| gdc.description.startpage | 3674 | en_US |
| gdc.description.volume | 43 | en_US |
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| gdc.oaire.keywords | Support vector machines | |
| gdc.oaire.keywords | Abnormality detection | |
| gdc.oaire.keywords | Biomedical data analysis | |
| gdc.oaire.keywords | Electroencephalography | |
| gdc.oaire.keywords | Data flow analysis | |
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