Constraining the MLP power of expression to facilitate symbolic rule extraction

Extracting symbolic rules from multilayer perceptrons is an important open question, especially when input neurons are continuous. To solve this problem we constrain the power of expression of a standard MLP with threshold functions in the hidden layer. In this case, hyper-plane equations are precisely determined and translated into symbolic rules. We illustrate our interpretable MLP (IMLP) in two applications; one from iris classification, and one from coronary heart disease diagnosis. In spite of the reduced power of expression, IMLP is able to give close mean predictive accuracy with respect to a standard MLP