C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Unifying instance-based and rule-based induction
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
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
Solving regression problems with rule-based ensemble classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
An Implementation of Logical Analysis of Data
IEEE Transactions on Knowledge and Data Engineering
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Machine Learning
Classification Rule Learning with APRIORI-C
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Algorithm for Induction of Decision Rules Consistent with the Dominance Principle
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning
Fundamenta Informaticae
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Heuristic rule-based regression via dynamic reduction to classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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
Multi-target regression with rule ensembles
The Journal of Machine Learning Research
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We introduce a novel decision rule induction algorithm for solving the regression problem. There are only few approaches in which decision rules are applied to this type of prediction problems. The algorithm uses a single decision rule as a base classifier in the ensemble. Forward stagewise additive modeling is used in order to obtain the ensemble of decision rules. We consider two types of loss functions, the squared- and absolute-error loss, that are commonly used in regression problems. The minimization of empirical risk based on these loss functions is performed by two optimization techniques, the gradient boosting and the least angle technique. The main advantage of decision rules is their simplicity and good interpretability. The prediction model in the form of an ensemble of decision rules is powerful, which is shown by results of the experiment presented in the paper.