Construction and Refinement of Preference Ordered Decision Classes

Abstract

Preference learning methods are commonly used in multicriteria analysis. The working principle of these methods is similar to classical machine learning techniques. A common issue to both machine learning and preference learning methods is the difficulty of the definition of decision classes and the assignment of objects to these classes, especially for large datasets. This paper proposes two procedures permitting to automatize the construction of decision classes. It also proposes two simple refinement procedures, that rely on the 80-20 principle, permitting to map the output of the construction procedures into a manageable set of decision classes. The proposed construction procedures rely on the most elementary preference relation, namely dominance relation, which avoids the need for additional information or distance/(di)similarity functions, as with most of existing clustering methods. Furthermore, the simplicity of the 80-20 principle on which the refinement procedures are based, make them very adequate to large datasets. Proposed procedures are illustrated and validated using real-world datasets.