Machine Learning
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Rule-based machine learning methods for functional prediction
Journal of Artificial Intelligence Research
Hi-index | 0.00 |
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.