Learning Rule Ensembles for Ordinal Classification with Monotonicity Constraints

Issue title: Fundamentals of Knowledge Technology

Article type: Research Article

Authors: Dembczyński, Krzysztof | Kotłowski., Wojciech | Słowiński, Roman^;

Affiliations: Institute of Computing Science, Poznań University of Technology, 60-965 Poznań, Poland. E-mail: {kdembczynski,kotlowski}@cs.put.poznan.pl | Systems Research Institute, Polish Academy of Sciences, 01-447 Warsaw, Poland. E-mail: [email protected]

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.

Keywords: ordinal classification, monotonicity constraints, rule ensembles, forward stagewise additive modeling, boosting, dominance-based rough set approach

DOI: 10.3233/FI-2009-124

Journal: Fundamenta Informaticae, vol. 94, no. 2, pp. 163-178, 2009