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| Improving the classification accuracy using biomarkers selected from machine learning methods |
| LinduniM.Rodrigo,AshokaD.Polpitiya |
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| (1 Department of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia;2 Colombo School of Business and Management, Colombo, Sri Lanka) |
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| 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 |
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| 基金项目: |
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| 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 |
