In the ordinal classification with monotonicity constraints, it is assumed that the class label should increase with increasing values on the attributes. In this paper we aim at formalizing the approach to learning with monotonicity constraints from statistical point of view, which results in the algorithm for learning rule ensembles. The algorithm first “monotonizes” the data using a nonparametric classification procedure and then generates rule ensemble consistent with the training set. The procedure is justified by a theoretical analysis and verified in a computational experiment.
Discussion