Feature Selection with Mutual Information for Regression Problems

Selecting relevant features for machine learning modeling improves the performance of the learning methods. Mutual information (MI) is known to be used as relevant criterion for selecting feature subsets from input dataset with a nonlinear relationship to the predicting attribute. However, mutual information estimator suffers the following limitation; it depends on smoothing parameters, the feature selection greedy methods lack theoretically justified stopping criteria and in theory it can be used for both classification and regression problems, however in practice more often it formulation is limited to classification problems. This paper investigates a proposed improvement on the three limitations of the Mutual Information estimator (as mentioned above), through the use of resampling techniques and formulation of mutual information based on differential entropic for regression problems