A Copula Statistic for Measuring Nonlinear Dependence with Application to Feature Selection in Machine Learning
نویسندگان
چکیده
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. This paper introduces a new index based on empirical copulas, termed as the Copula Statistic (CoS) to assess the strength of statistical dependence and for testing statistical independence. It shows that this test exhibits higher statistical power than other indices. Finally, applying the CoS features selection in machine learning problems, which allow a demonstration of the good performance of the CoS. Keywords—Copula; multivariate dependence; nonlinear systems; feature selection; machine learning
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