Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification

作者：

Highlights：

• The CBPLR showed superior results in terms of AUR and misclassification rate.

• In terms of the number of selected genes, the CBPLR outperformed APLR and LASSO.

• The CBPLR performed remarkably well in stability test.

• The classification accuracy for the CBPLR method is quite consistent and high.

摘要

•The CBPLR showed superior results in terms of AUR and misclassification rate.•In terms of the number of selected genes, the CBPLR outperformed APLR and LASSO.•The CBPLR performed remarkably well in stability test.•The classification accuracy for the CBPLR method is quite consistent and high.

论文关键词：Adaptive LASSO,Penalized logistic regression,Cancer classification,Gene selection

论文评审过程：Received 7 March 2015, Revised 5 August 2015, Accepted 12 August 2015, Available online 20 August 2015, Version of Record 9 September 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.08.016