Performance of Classification for Adjusted Adaptive Elastic Net Penalty and Adaptive LASSO for Breast Cancer Data

Abstract

      In The current time witnesses a significant surge in data, fueled by technology's rapid advancement. This increase in data volume has led to the emergence of high-dimensional data (where the number of variables exceeds the sample size), creating challenges in precision and target identification. Consequently, binary response variable classification becomes intricate due to the multicollinearity in explanatory variables. To tackle this, response variable classification has prompted the utilization of penalization techniques, reduced variables and selecting best variables in the model. This aids in simplifying the model complexity to attain the specific binary outcome (0,1). In this paper, penalization methods were applied, including Adaptive Least Absolute Shrinkage and Selection Operator, With the Adjusted Adaptive Elastic Net Penalty with logistic regression model. The application involved a set of real data. A sample collected by the researcher (p=49, n=41), and it produced positive results for classification in the sample collected by the researcher, in resulted we found these methods have achieving high classification accuracy while efficiently selecting an optimal number of variables using a range of packages and functions in the R programming language.