Quasi-supervised learning for biomedical data analysis

PATTERN RECOGNITION

卷:43

Article

Karacali, Bilge

关键词: Biomedical data analysis;    Abnormality detection;    Nearest neighbor rule;    Support vector machines;    Flow cytometry;    Electroencephalography;   

DOI  :  10.1016/j.patcog.2010.04.024

来源: Elsevier

【 摘 要 】

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. (C) 2010 Elsevier Ltd. All rights reserved.

【 授权许可】

Free