Learning in the presence of malicious errors
SIAM Journal on Computing
Trading Accuracy for Simplicity in Decision Trees
Machine Learning
An introduction to computational learning theory
An introduction to computational learning theory
Encouraging Experimental Results on Learning CNF
Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Efficient algorithms for constructing decision trees with constraints
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
IEEE Expert: Intelligent Systems and Their Applications
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Learning Decision Rules by Randomized Iterative Local Search
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
The Effects of Training Set Size on Decision Tree Complexity
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Quantitative Study of Small Disjuncts
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
On the difficulty of approximately maximizing agreements
Journal of Computer and System Sciences
A report on experiments with weighted relative accuracy in CN2
A report on experiments with weighted relative accuracy in CN2
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Application of information theory to the construction of efficient decision trees
IEEE Transactions on Information Theory
Evaluating pattern set mining strategies in a constraint programming framework
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Model selection in omnivariate decision trees using Structural Risk Minimization
Information Sciences: an International Journal
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While recent research on rule learning has focused largely on finding highly accurate hypotheses, we evaluate the degree to which these hypotheses are also simple, that is small. To realize this, we compare well-known rule learners, such as CN2, RIPPER, PART, FOIL and C5.0 rules, with the benchmark system SL^2 that explicitly aims at computing small rule sets with few literals. The results show that it is possible to obtain a similar level of accuracy as state-of-the-art rule learners using much smaller rule sets.