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Published 1998 | Published
Book Section - Chapter Open

Monotonic Networks


Monotonicity is a constraint which arises in many application domains. We present a machine learning model, the monotonic network, for which monotonicity can be enforced exactly, i.e., by virtue of functional form . A straightforward method for implementing and training a monotonic network is described. Monotonic networks are proven to be universal approximators of continuous, differentiable monotonic functions. We apply monotonic networks to a real-world task in corporate bond rating prediction and compare them to other approaches.

Additional Information

© 1998 Massachusetts Institute of Technology. The author is very grateful to Yaser Abu-Mostafa for considerable guidance. I also thank John Moody for supplying the data. Amir Atiya, Eric Bax, Zehra Cataltepe, Malik Magdon-Ismail, Alexander Nicholson, and Xubo Song supplied many useful comments.

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