Machine Learning - special issue on inductive logic programming
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Machine Learning
SBIA '96 Proceedings of the 13th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Predictive Performance of Weghted Relative Accuracy
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Generating Rule Sets from Model Trees
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Rule Evaluation Measures: A Unifying View
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
A tutorial on support vector regression
Statistics and Computing
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Solving Regression by Learning an Ensemble of Decision Rules
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Rule-based machine learning methods for functional prediction
Journal of Artificial Intelligence Research
On the quest for optimal rule learning heuristics
Machine Learning
Learning predictive clustering rules
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Rule quality measure-based induction of unordered sets of regression rules
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
Evaluating data mining algorithms using molecular dynamics trajectories
International Journal of Data Mining and Bioinformatics
An evolutionary method for associative local distribution rule mining
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
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In this paper, we propose a novel approach for learning regression rules by transforming the regression problem into a classification problem. Unlike previous approaches to regression by classification, in our approach the discretization of the class variable is tightly integrated into the rule learning algorithm. The key idea is to dynamically define a region around the target value predicted by the rule, and considering all examples within that region as positive and all examples outside that region as negative. In this way, conventional rule learning heuristics may be used for inducing regression rules. Our results show that our heuristic algorithm outperforms approaches that use a static discretization of the target variable, and performs en par with other comparable rule-based approaches, albeit without reaching the performance of statistical approaches.