A Framework for Adaptive and Integrated Classification
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Large scale data mining using genetics-based machine learning
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Facetwise analysis of XCS for problems with class imbalances
IEEE Transactions on Evolutionary Computation
Parallel distributed implementation of genetics-based machine learning for fuzzy classifier design
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Large scale data mining using genetics-based machine learning
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Towards a mapping of modern AIS and LCS
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Self-adaptation of parameters in a learning classifier system ensemble machine
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
Large scale data mining using genetics-based machine learning
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Ensemble fuzzy rule-based classifier design by parallel distributed fuzzy GBML algorithms
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Selection strategy for XCS with adaptive action mapping
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Large scale data mining using genetics-based machine learning
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Just over thirty years after Holland first presented the outline for Learning Classifier System paradigm, the ability of LCS to solve complex real-world problems is becoming clear. In particular, their capability for rule induction in data mining has sparked renewed interest in LCS. This book brings together work by a number of individuals who are demonstrating their good performance in a variety of domains. The first contribution is arranged as follows: Firstly, the main forms of LCS are described in some detail. A number of historical uses of LCS in data mining are then reviewed before an overview of the rest of the volume is presented. The rest of this book describes recent research on the use of LCS in the main areas of machine learning data mining: classification, clustering, time-series and numerical prediction, feature selection, ensembles, and knowledge discovery.