Multiclassification to Gene Expression Data with Some Complex Features

Li-Pang Chen
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Li-Pang Chen: Department of Statistics and Actuarial Science, University of Waterloo, Canada

Biostatistics and Biometrics Open Access Journal, 2018, vol. 9, issue 1, 1-2

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.

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:9:y:2018:i:1:p:1-2

DOI: 10.19080/BBOAJ.2018.09.555751

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