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