Employing linear regression in regression tree leaves
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Rule-based machine learning methods for functional prediction
Journal of Artificial Intelligence Research
Expert Systems with Applications: An International Journal
Algorithm and Feature Selection for VegOut: A Vegetation Condition Prediction Tool
DS '09 Proceedings of the 12th International Conference on Discovery Science
Behavioural rule discovery from swarm systems
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Predicting personality with social media
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Supporting system for detecting pathologies
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Exploiting macro-actions and predicting plan length in planning as satisfiability
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Computers and Electronics in Agriculture
Unsupervised generation of data mining features from linked open data
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
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
Can we use linked data semantic annotators for the extraction of domain-relevant expressions?
Proceedings of the 22nd international conference on World Wide Web companion
Predicting cardiac autonomic neuropathy category for diabetic data with missing values
Computers in Biology and Medicine
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Model trees--decision trees with linear models at the leaf nodes--have recently emerged as an ax;curate method for numeric prediction that produces understandable models. However, it is known that decision lists--ordered sets of If-Then rules--have the potential to be more compact and therefore more understandable than their tree counterparts. We present an algorithm for inducing simple, accurate decision lists from model trees. Model trees are built repeatedly and the best rule is selected at each iteration. This method produces rule sets that are as accurate but smaller than the model tree constructed from the entire dataset. Experimental results for various heuristics which attempt to find a compromise between rule accuracy and rule coverage are reported. We show that our method produces comparably accurate and smaller rule sets than the commercial state-of-the-art rule learning system Cubist.