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On the Performance of Variable Selection and Classification via Rank-Based Classifier

Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Mathematics 2019, 7(5), 457;
Received: 26 April 2019 / Revised: 11 May 2019 / Accepted: 14 May 2019 / Published: 21 May 2019
(This article belongs to the Special Issue Uncertainty Quantification Techniques in Statistics)
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In high-dimensional gene expression data analysis, the accuracy and reliability of cancer classification and selection of important genes play a very crucial role. To identify these important genes and predict future outcomes (tumor vs. non-tumor), various methods have been proposed in the literature. But only few of them take into account correlation patterns and grouping effects among the genes. In this article, we propose a rank-based modification of the popular penalized logistic regression procedure based on a combination of 1 and 2 penalties capable of handling possible correlation among genes in different groups. While the 1 penalty maintains sparsity, the 2 penalty induces smoothness based on the information from the Laplacian matrix, which represents the correlation pattern among genes. We combined logistic regression with the BH-FDR (Benjamini and Hochberg false discovery rate) screening procedure and a newly developed rank-based selection method to come up with an optimal model retaining the important genes. Through simulation studies and real-world application to high-dimensional colon cancer gene expression data, we demonstrated that the proposed rank-based method outperforms such currently popular methods as lasso, adaptive lasso and elastic net when applied both to gene selection and classification. View Full-Text
Keywords: gene-expression data; 2 ridge; 1 lasso; adapative lasso; elastic net; BH-FDR; Laplacian matrix gene-expression data; 2 ridge; 1 lasso; adapative lasso; elastic net; BH-FDR; Laplacian matrix

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Sarker, M.S.R.; Pokojovy, M.; Kim, S. On the Performance of Variable Selection and Classification via Rank-Based Classifier. Mathematics 2019, 7, 457.

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