Proceedings of the seventh international conference (1990) on Machine learning
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Classifier Systems and the Animat Problem
Machine Learning
A Tale of Two Classifier Systems
Machine Learning
Knowledge Growth in an Artificial Animal
Proceedings of the 1st International Conference on Genetic Algorithms
A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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A stimulus-response learning classifier system (LCS), EpiCS, was developed from the BOOLE and NEWBOOLE models to address the needs of knowledge discovery in databases used in clinical research. Two specific needs were investigated: the derivation of accurate estimates of disease risk, and the ability to deal with rare clinical outcomes. EpiCS was shown to have excellent classification accuracy, compared to logistic regression, when using risk estimates as the primary means for classification. This was especially true in data with low disease prevalence. EpiCS was designed to accommodate differential negative reinforcement when false positive or false negative decisions were made by the system. This feature was investigated to determine its effect on learning rate and classification accuracy. Tested across a range of disease prevalences, the learning rate improved when erroneous decisions were differentially negatively reinforced. However, classification accuracy was not affected by differential negative reinforcement.