Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization
Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China
Author to whom correspondence should be addressed.
Received: 8 August 2018 / Revised: 29 August 2018 / Accepted: 4 September 2018 / Published: 6 September 2018
In recent years, gene selection for cancer classification based on the expression of a small number of gene biomarkers has been the subject of much research in genetics and molecular biology. The successful identification of gene biomarkers will help in the classification of different types of cancer and improve the prediction accuracy. Recently, regularized logistic regression using the
regularization has been successfully applied in high-dimensional cancer classification to tackle both the estimation of gene coefficients and the simultaneous performance of gene selection. However, the
has a biased gene selection and dose not have the oracle property. To address these problems, we investigate
regularized logistic regression for gene selection in cancer classification. Experimental results on three DNA microarray datasets demonstrate that our proposed method outperforms other commonly used sparse methods
in terms of classification performance.
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MDPI and ACS Style
Wu, S.; Jiang, H.; Shen, H.; Yang, Z. Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization. Appl. Sci. 2018, 8, 1569.
Wu S, Jiang H, Shen H, Yang Z. Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization. Applied Sciences. 2018; 8(9):1569.
Wu, Shengbing; Jiang, Hongkun; Shen, Haiwei; Yang, Ziyi. 2018. "Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization." Appl. Sci. 8, no. 9: 1569.
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