@article{(Open Science Index):https://publications.waset.org/pdf/2246,
	  title     = {Mixtures of Monotone Networks for Prediction},
	  author    = {Marina Velikova and  Hennie Daniels and  Ad Feelders},
	  country	= {},
	  institution	= {},
	  abstract     = {In many data mining 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. In this
paper we consider partially monotone prediction problems, where the
target variable depends monotonically on some of the input variables
but not on all. We propose a novel method to construct prediction
models, where monotone dependences with respect to some of
the input variables are preserved by virtue of construction. Our
method belongs to the class of mixture models. The basic idea is to
convolute monotone neural networks with weight (kernel) functions
to make predictions. By using simulation and real case studies,
we demonstrate the application of our method. To obtain sound
assessment for the performance of our approach, we use standard
neural networks with weight decay and partially monotone linear
models as benchmark methods for comparison. The results show that
our approach outperforms partially monotone linear models in terms
of accuracy. 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.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {1},
	  number    = {10},
	  year      = {2007},
	  pages     = {3378 - 3388},
	  ee        = {https://publications.waset.org/pdf/2246},
	  url   	= {https://publications.waset.org/vol/10},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 10, 2007},
	}