Bucket brigade performance: II. Default hierarchies
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Bid competition and specificity reconsidered
Complex Systems
Classifier systems and genetic algorithms
Artificial Intelligence
Triggered rule discovery in classifier systems
Proceedings of the third international conference on Genetic algorithms
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Competition-Based Induction of Decision Models from Examples
Machine Learning - Special issue on genetic algorithms
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Knowledge Growth in an Artificial Animal
Proceedings of the 1st International Conference on Genetic Algorithms
Further Research on Feature Selection and Classification Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
NeuroRule: A Connectionist Approach to Data Mining
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Zcs: A zeroth level classifier system
Evolutionary Computation
Using coverage as a model building constraint in learning classifier systems
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Search-intensive concept induction
Evolutionary Computation
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Initial Modifications to XCS for Use in Interactive Evolutionary Design
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
What Is a Learning Classifier System?
Learning Classifier Systems, From Foundations to Applications
A Roadmap to the Last Decade of Learning Classifier System Research
Learning Classifier Systems, From Foundations to Applications
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
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
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
Extracting User Profiles from E-mails Using the Set-Oriented Classifier
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Can evolutionary computation handle large datasets? a study into network intrusion detection
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
A performance study of gaussian kernel classifiers for data mining applications
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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It has been known for some time that Learning Classifier Systems (LCS) have potential for application as Data Mining tools. Parodi and Bonelli applied the Boole LCS to the Lymphography data set and reported 82% classification rates. More recent work, such as GA-Miner has sought to extend the application of the GA-based classification system to larger commercial data sets, introducing more complex attribute encoding techniques, static niching, and hybrid genetic operators in order to address the problems presented by large search spaces. Despite these results, the traditional LCS formulation has shown itself to be unreliable in the formation of accurate optimal generalisations, which are vital for the reduction of results to a human readable form. XCS has been shown to be capable of generating a complete and optimally accurate mapping of a test environment and therefore presents a new opportunity for the application of Learning Classifier Systems to the classification task in Data Mining. As part of a continuing research effort this paper presents some first results in the application of XCS to a particular Data Mining task. It demonstrates that XCS is able to produce a classification performance and rule set which exceeds the performance of most current Machine Learning techniques when applied to the Monk's problems.