Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Bayesian estimation of rule accuracy in UCS
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
A principled foundation for LCS
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Bayesian estimation of rule accuracy in UCS
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
A Principled Foundation for LCS
Learning Classifier Systems
Revisiting UCS: Description, Fitness Sharing, and Comparison with XCS
Learning Classifier Systems
Intrusion detection with evolutionary learning classifier systems
Natural Computing: an international journal
A grid data mining architecture for learning classifier systems
WSEAS Transactions on Computers
Supervised learning classifier systems for grid data mining
CIS'09 Proceedings of the international conference on Computational and information science 2009
Fuzzy-UCS: a Michigan-style learning fuzzy-classifier system for supervised learning
IEEE Transactions on Evolutionary Computation
Accuracy exponentiation in UCS and its effect on voting margins
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Analysis of the niche genetic algorithm in learning classifier systems
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Credit assignment is a fundamental issue for the Learning Classifier Systems literature. We engage in a detailed investigation of credit assignment in one recent system called UCS, and in the process uncover two previously undocumented features. We draw on techniques from the classical pattern recognition literature, showing how to analytically derive an optimal credit assignment system, given certain assumptions. Our primary aim is not to improve accuracy, but to better understand the system and put it on a more solid theoreticalfoundation. Nonetheless, empirical results on benign data demonstrate our new system, called UCSpv (UCS with principled voting), can match or exceed the original UCS. Further, its fitness function is principled, and, unlike that of UCS, requires no tuning. However, on more difficult data it seems UCSpv does need some form of tuning or correction. We believe the framework we adopt offers a promising new direction for LCS research, providing principled methods for action selection and bringing LCS closer to the mainstream pattern recognition literature.