Learning Rule Ensembles for Ordinal Classification with Monotonicity Constraints

Authors:
Krzysztof Dembczyński;Wojciech Kotłowski.;Roman Słowiński
Affiliations:
Institute of Computing Science, Poznań University of Technology, 60-965 Poznań, Poland. E-mail: {kdembczynski,kotlowski}@cs.put.poznan.pl;Institute of Computing Science, Poznań University of Technology, 60-965 Poznań, Poland. E-mail: {kdembczynski,kotlowski}@cs.put.poznan.pl;Inst. of Comp. Sci., Poznań Univ. of Technol., 60-965 Poznań, Poland. E-mail: {kdembczynski,kotlowski}@cs.put.poznan.pl and Sys. Res. Inst., Polish Acad. of Sci., 01-447 Warsaw, Poland. ...
Venue:
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Year:
2009

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Abstract

Ordinal classification problems with monotonicity constraints (also referred to as multicriteria classification problems) often appear in real-life applications, however, they are considered relatively less frequently in theoretical studies than regular classification problems. We introduce a rule induction algorithm based on the statistical learning approach that is tailored for this type of problems. The algorithm first monotonizes the dataset (excludes strongly inconsistent objects), using Stochastic Dominance-based Rough Set Approach, and then uses forward stagewise additive modeling framework for generating a monotone rule ensemble. Experimental results indicate that taking into account knowledge about order andmonotonicity constraints in the classifier can improve the prediction accuracy.