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Improving the classification accuracy using biomarkers selected from machine learning methods

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Abstract

High-dimensional data encountered in genomic and proteomic studies are often limited by the sample size but has a higher number of predictor variables. Therefore selecting the most relevant variables that are correlated with the outcome variable is a crucial step. This paper describes an approach for selecting a set of optimal variables to achieve a classification model with high predictive accuracy. The work described using a biological classifier published elsewhere but it can be generalized for any application.

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Authors and Affiliations

  1. Department of Mathematics and Statistics, University of Sydney, Sydney, NSW, 2006, Australia

    Linduni M. Rodrigo

  2. Colombo School of Business and Management, Colombo, Sri Lanka

    Ashoka D. Polpitiya

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  1. Linduni M. Rodrigo
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  2. Ashoka D. Polpitiya
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Correspondence to Linduni M. Rodrigo.

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Rodrigo, L.M., Polpitiya, A.D. Improving the classification accuracy using biomarkers selected from machine learning methods. Control Theory Technol. 19, 538–543 (2021). https://doi.org/10.1007/s11768-021-00071-x

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  • DOI: https://doi.org/10.1007/s11768-021-00071-x

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