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 forward stagewise additive modeling that is tailored for this type of problems. The algorithm monotonizes the dataset (excludes highly inconsistent objects) using Dominance-based Rough Set Approach and generates monotone rules. Experimental results indicate that taking into account the knowledge about order and monotonicity constraints in the classifier can improve the prediction accuracy.
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Dembczyński, K., Kotłowski, W., Słowiński, R. (2008). Ensemble of Decision Rules for Ordinal Classification with Monotonicity Constraints. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2008. Lecture Notes in Computer Science(), vol 5009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79721-0_38
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DOI: https://doi.org/10.1007/978-3-540-79721-0_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79720-3
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