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Multiclassification to Gene Expression Data with Some Complex Features

Author

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  • Li-Pang Chen

    (Department of Statistics and Actuarial Science, University of Waterloo, Canada)

Abstract

Classification is usually an important topic which mainly classifies subjects to their classes. Many methods, including machine learning theory, have been fully developed. In the era of big data, however, we may encounter the more complex dataset, and the conventional methods may either fail to solve problems or ignore some key perspectives. In this paper, we mainly focus on gene expression data and present some features which may appear in the dataset. In addition, we also outline some research directions and discuss possible solutions to deal with these problems.

Suggested Citation

  • Li-Pang Chen, 2018. "Multiclassification to Gene Expression Data with Some Complex Features," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(1), pages 1-2, December.
    • Handle: RePEc:adp:jbboaj:v:9:y:2018:i:1:p:1-2
    DOI: 10.19080/BBOAJ.2018.09.555751
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    References listed on IDEAS

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    1. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
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    Cited by:

    1. Li-Pang Chen, 2022. "Network-Based Discriminant Analysis for Multiclassification," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 410-431, November.

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