The selection of an appropriate subset of variables from a set of measured potential input variables for inclusion as inputs to model the system under investigation is a vital step in model development. Mutual Information (MI) has been used successfully to measure the dependence between output and input variables. In contrast to the linear correlation coefficient, which often forms the basis of empirical input variable selection approaches in linear model, mutual information is capable of measuring dependencies based on both linear and nonlinear model.
