Proceedings of the seventh international conference (1990) on 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
A New Bootstrapping Method to Improve Classification Performance in Learning Classifier Systems
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
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
XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
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A stimulus-response LCS, called EpiCS, based upon the BOOLE and NEWBOOLE paradigms, was developed to work in single-step environments in which the goal is to generalize clinical decision rules from medical data by means of building explanatory and predictive models. This paper addresses the scalability of EpiCS to a large database, the Fatal Accident Reporting System (FARS), which is a large prospective database supported by the National Highway Traffic Safety Administration (NHTSA) of Transportation. This investigation used 1998 FARS data, the most recent complete year's data available at this time. The performance of EpiCS in building explanatory and predictive models compared very favorably with a decision tree inducer and logistic regression applied to these tasks.