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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 bedoi:10.1109/cita.2015.7349826 dblp:conf/cita/SulaimanL15 fatcat:p3l4xomacrbwxbrzdt5vucfkhq