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Reduction of the high dimensional binary classification data using penalized logistic regression is one of the challenges when the explanatory variables are correlated. To tackle both estimating the coefficients and performing the variable selection simultaneously, elastic net penalty was successfully applied in high dimensional binary classification. However, elastic net has two major limitations. First it does not encourage grouping effects when there is no high correlation. Second, it is notdoi:10.18187/pjsor.v11i4.990 fatcat:gsz7tcprybgefm34kfebidbqhm