Abstract
This paper presents the algorithm for induction of unordered sets of regression rules. It uses sequential covering strategy and dynamic reduction to classification approach. The main focus is put on quality measures which control the process of rule induction. We examined the effectiveness of nine quality measures. Moreover, we propose and compare three schemes of the prediction of target attribute value of examples covered by more than one rule. We also show rule filtration algorithm for the reduction of the number of generated rules. All experiments were carried out on 35 benchmark datasets.
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Sikora, M., Skowron, A., Wróbel, Ł. (2012). Rule Quality Measure-Based Induction of Unordered Sets of Regression Rules. In: Ramsay, A., Agre, G. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2012. Lecture Notes in Computer Science(), vol 7557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33185-5_18
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DOI: https://doi.org/10.1007/978-3-642-33185-5_18
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