Classification trees for problems with monotonicity constraints

Abstract

For classification problems with ordinal attributes very often the class attribute should increase with each or some of the explaining attributes. These are called classification problems with monotonicity constraints. Classical decision tree algorithms such as CART or C4.5 generally do not produce monotone trees, even if the dataset is completely monotone. This paper surveys the methods that have so far been proposed for generating decision trees that satisfy monotonicity constraints. A distinction is made between methods that work only for monotone datasets and methods that work for monotone and non-monotone datasets alike.