A Robust-Equitable Copula Dependence Measure for Feature Selection


Yale Chang, Yi Li, Adam Ding, Jennifer Dy ;
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR 51:84-92, 2016.


Feature selection aims to select relevant features to improve the performance of predictors. Many feature selection methods depend on the choice of dependence measures. To select features that have complex nonlinear relationships with the response variable, the dependence measure should be equitable: treating linear and nonlinear relationships equally. In this paper we introduce the concept of robust-equitability and a robust-equitable dependence measure copula correlation (Ccor). This measure has the following advantages compared to existing dependence measures: it is robust to different relationship forms and robust to unequal sample sizes of different features. In contrast, existing dependence measures cannot take these factors into account simultaneously. Experiments on synthetic and real-world datasets confirm our theoretical analysis, and illustrates its advantage in feature selection.

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