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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Improving Classification for Microarray Data Sets by Constructing Synthetic Data
verfasst von : Shun Bian, Wenjia Wang
Erschienen in: Computational Intelligence and Security
Verlag: Springer Berlin Heidelberg
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Microarray technology has been widely used in biological and medical research to observe a large number of gene expressions. However, such experiments are usually carried out with few replica or instances, which may lead to poor modelling and analysis. This paper suggests an approach to improve classification by using synthetic data. A new algorithm is proposed to estimate synthetic data value and the generated data are labelled by ensemble methods. Experiments with artificial data and real world data demonstrate that the proposed algorithm is able to generate synthetic data on uncertain regions of classifiers to improve effectiveness and efficiency of classification on microarray data sets.