Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
State of XCS Classifier System Research
Learning Classifier Systems, From Foundations to Applications
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
Learning Classifier Systems, From Foundations to Applications
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Classifiers that approximate functions
Natural Computing: an international journal
Initial Modifications to XCS for Use in Interactive Evolutionary Design
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
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
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
MOLeCS: Using Multiobjective Evolutionary Algorithms for Learning
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
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
Learning classifier system ensemble for data mining
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Analysis of Population Evolution in Classifier Systems Using Symbolic Representations
Learning Classifier Systems
Evolving Classifiers Ensembles with Heterogeneous Predictors
Learning Classifier Systems
On Dynamical Genetic Programming: Random Boolean Networks in Learning Classifier Systems
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
To handle real valued input in XCS: using fuzzy hyper-trapezoidal membership in classifier condition
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Towards final rule set reduction in XCS: a fuzzy representation approach
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Fast prediction computation in learning classifier systems using CUDA
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Random artificial incorporation of noise in a learning classifier system environment
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Extracting adaptation strategies for e-learning programs with XCS
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Co-evolutionary rule-chaining genetic programming
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Post-processing operators for decision lists
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Extracting and using building blocks of knowledge in learning classifier systems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Two-cornered learning classifier systems for pattern generation and classification
Proceedings of the 14th annual conference on Genetic and evolutionary computation
XCSR with computed continuous action
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
The subsumption mechanism for XCS using code fragmented conditions
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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The classifier system XCS was investigated for data mining applications where the dataset discrimination surface (DS) is generally oblique to the attribute axes. Despite the classifiers' hyper-rectangular predicates, XCS reached 100% performance on synthetic problems with diagonal DS's and, in a train/test experiment, competitive performance on the Wisconsin Breast Cancer dataset. Final classifiers in an extended WBC learning run were interpretable to suggest dependencies on one or a few attributes. For data mining of numeric datasets with partially oblique discrimination surfaces, XCS shows promise from both performance and pattern discovery viewpoints.