A Copula Statistic for Measuring Nonlinear Dependence with Application to Feature Selection in Machine Learning

Abstract

Feature selection in machine learning aims to find out the best subset of variables from the input that reduces the computation requirement and improves the predictor performance. In this paper, a new index based on empirical copulas, termed the Copula Statistic (CoS) to assess the strength of statistical dependence and for testing statistical independence is introduced. It is shown that this test exhibits higher statistical power than other indices. Finally, the CoS is applied to feature selection in machine learning problems, which allow a demonstration of the good performance of the CoS.