Abstract
We introduce the concept of variable-consistency monotonic decision tree induced from preference-ordered data concerning a multicriteria sorting (classification) problem. Given the data in form of an information table including some sorting examples, we propose to induce a decision tree using an inductive learning algorithm. The decision tree can be considered as a preference model of a decision maker who supplied the sorting examples. Moreover, a partial violation of the dominance principle is admitted and controlled by an index called consistency level. The monotonic decision trees with variable consistency can be applied to a wide range of possible applications, for instance, financial rating, bank creditworthiness, medical diagnosis, and the like.
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