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- W2002258118 abstract "In 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. Motivated by the statistical analysis, we present an algorithm for learning rule ensembles. The algorithm first monotonizes the data using a nonparametric classification procedure and then generates a rule ensemble consistent with the training set. The procedure is justified by a theoretical analysis and verified in a computational experiment." @default.
- W2002258118 created "2016-06-24" @default.
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- W2002258118 date "2009-06-14" @default.
- W2002258118 modified "2023-10-16" @default.
- W2002258118 title "Rule learning with monotonicity constraints" @default.
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- W2002258118 doi "https://doi.org/10.1145/1553374.1553444" @default.
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