The monotonicity constraint is a common side condition imposed on
modeling problems as diverse as hedonic pricing, personnel
selection and credit rating. Experience tells us that it is not
trivial to generate artificial data for supervised learning
problems when the monotonicity constraint holds. Two algorithms
are presented in this paper for such learning problems. The first
one can be used to generate random monotone data sets without an
underlying model, and the second can be used to generate monotone
decision tree models. If needed, noise can be added to the
generated data. The second algorithm makes use of the first one.
Both algorithms are illustrated with an example