Rule learning with monotonicity constraints

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Rule learning with monotonicity constraints
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.
Wojciech Kotlowski, Roman Slowinski
Added 19 May 2010
Updated 19 May 2010
Type Conference
Year 2009
Where ICML
Authors Wojciech Kotlowski, Roman Slowinski
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