Rule learning with monotonicity constraints

Authors:
Wojciech Kotłowski;Roman Słowiński
Affiliations:
Poznań University of Technology, Poznań, Poland;Poznań University of Technology, Poznań, Poland
Venue:
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Year:
2009

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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.