%0 Journal Article
	%A Marina Velikova and  Hennie Daniels and  Ad Feelders
	%D 2007
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 12, 2007
	%T Solving Partially Monotone Problems with Neural Networks
	%U https://publications.waset.org/pdf/11728
	%V 12
	%X In many applications, it is a priori known that the
target function should satisfy certain constraints imposed by, for
example, economic theory or a human-decision maker. Here we
consider partially monotone problems, where the target variable
depends monotonically on some of the predictor variables but not all.
We propose an approach to build partially monotone models based
on the convolution of monotone neural networks and kernel
functions. The results from simulations and a real case study on
house pricing show that our approach has significantly better
performance than partially monotone linear models. Furthermore, the
incorporation of partial monotonicity constraints not only leads to
models that are in accordance with the decision maker's expertise,
but also reduces considerably the model variance in comparison to
standard neural networks with weight decay.
	%P 4043 - 4048