Quasi-supervised learning for biomedical data analysis

Authors:
Bilge Karaçalı
Affiliations:
Electrical and Electronics Engineering Department, Izmir Institute of Technology Urla, Izmir 35430, Turkey
Venue:
Pattern Recognition
Year:
2010

Citing 6
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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.