Elements of machine learning
Machine Learning - Special issue on inductive transfer
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Expert-guided subgroup discovery: methodology and application
Journal of Artificial Intelligence Research
Learning predictive clustering rules
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
MulO-AntMiner: a new ant colony algorithm for the multi-objective classification problem
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part II
Predicting structured outputs k-nearest neighbours method
DS'11 Proceedings of the 14th international conference on Discovery science
Iterative classification for multiple target attributes
Journal of Intelligent Information Systems
Multi-target regression with rule ensembles
The Journal of Machine Learning Research
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Among predictive models, 'if-then' rule sets are one of the most expressive and human readable model representations. Most of the existing approaches for rule learning focus on predicting a single target attribute/class. In practice, however, we encounter many problems where the task is to predict not one, but several related target attributes. We employ the predictive clustering approach to learn rules for simultaneous prediction of multiple target attributes. We propose a new rule learning algorithm, which (unlike existing rule learning approaches) generalizes to multiple target prediction. We empirically evaluate the new method and show that rule sets for multiple target prediction yield comparable accuracy to the respective collection of single target rule sets. The size of the multiple target rule set, however, is much smaller than the total size of the collection of single target rule sets.