We describe a lightweight learning method that induces an ensemble of decision-rule solutions for regression problems. Instead of direct prediction of a continuous output variable, the method discretizes the variable by k-means clustering and solves the resultant classification problem. Predictions on new examples are made by averaging the mean values of classes with votes that are close in number to the most likely class. We provide experimental evidence that this indirect approach can often yield strong results for many applications, generally outperforming direct approaches such as regression trees and rivaling bagged regression trees. © Springer-Verlag Berlin Heidelberg 2001.
CITATION STYLE
Indurkhya, N., & Weiss, S. M. (2001). Rule-based ensemble solutions for regression. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2123 LNAI, pp. 62–72). Springer Verlag. https://doi.org/10.1007/3-540-44596-x_6
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