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Improving the classification accuracy using biomarkers selected from machine learning methods
LinduniM.Rodrigo,AshokaD.Polpitiya
0
(1 Department of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia;2 Colombo School of Business and Management, Colombo, Sri Lanka)
摘要:
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.
关键词:  Classification · Variable selection · Reversal · Regression
DOI:https://doi.org/10.1007/s11768-021-00071-x
基金项目:
Improving the classification accuracy using biomarkers selected from machine learning methods
Linduni M. Rodrigo,Ashoka D. Polpitiya
(1 Department of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia;2 Colombo School of Business and Management, Colombo, Sri Lanka)
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.
Key words:  Classification · Variable selection · Reversal · Regression