Classification Trees for Problems with Monotonicity Constraints

For classification problems with ordinal attributes very often theclass attribute should increase with each or some of theexplaining attributes. These are called classification problemswith monotonicity constraints. Classical decision tree algorithmssuch as CART or C4.5 generally do not produce monotone trees, evenif the dataset is completely monotone. This paper surveys themethods that have so far been proposed for generating decisiontrees that satisfy monotonicity constraints. A distinction is madebetween methods that work only for monotone datasets and methodsthat work for monotone and non-monotone datasets alike