Proceedings of the seventh international conference (1990) 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
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Classifier fitness based on accuracy
Evolutionary Computation
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The increasing use of learning classifier systems (LCS) in data mining necessitates a methodology for improving the assessment of predictive accuracy at both the individual classifier and system levels. A metric, predictive value, is used extensively in clinical diagnosis and medical decision making, and is easily adapted to the LCS to facilitate assessing the ability of individual classifiers used as rules to predict class membership. This metric can also be used to assess the ability of a trained LCS to predict the class of unseen cases. Positive and predictive values were incorporated into an existing LCS model, EpiCS, and applied to 6-Multiplexer data and a sample data set drawn from a large hospitalization survey. The predictive performance of EpiCS on the hospitalization data was shown to be comparable to that of logistic regression and decision tree induction.