Probabilistic rough set approaches to ordinal classification with monotonicity constraints

Abstract

We present some probabilistic rough set approaches to ordinal classification with monotonicity constraints, where it is required that the class label of an object does not decrease when evaluation of this object on attributes improves. Probabilistic rough set approaches allow to structure the classification data prior to induction of decision rules. We apply sequential covering to induce rules that satisfy consistency constraints. These rules are then used to make predictions on a new set of objects. After discussing some interesting features of this type of reasoning about ordinal data, we perform an extensive computational experiment to show a practical value of this proposal which is compared to other well known methods. © 2010 Springer-Verlag Berlin Heidelberg.