Rule Learning with Monotonicity Constraints
published: Aug. 26, 2009, recorded: June 2009, views: 2759
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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.
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