Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Proceedings of the seventh international conference (1990) on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Predictive data mining: a practical guide
Predictive data mining: a practical guide
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Tale of Two Classifier Systems
Machine Learning
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Boosting a Strong Learner: Evidence Against the Minimum Margin
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
A Representation for Accuracy-Based Assessment of Classifier System Prediction Performance
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
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A new technique for improving the classification performance of learning classifier systems (LCS) was developed and applied to a real-world data mining problem. EpiCS, a stimulus-response LCS, was adapted to perform prevalence-based bootstrapping, wherein data from training and testing sets were sampled according to the prevalence of the individual classes, rather than randomly using the class distribution inherent in the data. Prevalence-based bootstrapping was shown to improve classification performance significantly on training and testing. Furthermore, this procedure was shown to enhance EpiCS's classification performance on testing compared to a well-known decision tree inducer (C4.5) when similar bootstrapping procedures were applied to the latter.