An analysis of matching in learning classifier systems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Measuring the Applicability of Self-organization Maps in a Case-Based Reasoning System
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
When Similar Problems Don't Have Similar Solutions
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
A Methodology for Analyzing Case Retrieval from a Clustered Case Memory
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Data Complexity Analysis: Linkage between Context and Solution in Classification
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Analysis and improvement of the genetic discovery component of XCS
International Journal of Hybrid Intelligent Systems - Data Mining and Hybrid Intelligent Systems
Modeling Problem Transformations based on Data Complexity
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
Performance evaluation of evolutionary algorithms in classification of biomedical datasets
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
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
Domains of Competence of Artificial Neural Networks Using Measures of Separability of Classes
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Integration of a Methodology for Cluster-Based Retrieval in jColibri
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy
Evolutionary Computation
Fuzzy-UCS: a Michigan-style learning fuzzy-classifier system for supervised learning
IEEE Transactions on Evolutionary Computation
The class imbalance problem in UCS classifier system: a preliminary study
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
IEEE Transactions on Evolutionary Computation
Large scale data mining using genetics-based machine learning
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Information Sciences: an International Journal
Linear separability and classification complexity
Expert Systems with Applications: An International Journal
Large scale data mining using genetics-based machine learning
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Analysis of data complexity measures for classification
Expert Systems with Applications: An International Journal
Large scale data mining using genetics-based machine learning
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Towards UCI+: A mindful repository design
Information Sciences: an International Journal
Domains of competence of the semi-naive Bayesian network classifiers
Information Sciences: an International Journal
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The XCS classifier system has recently shown a high degree of competence on a variety of data mining problems, but to what kind of problems XCS is well and poorly suited is seldom understood, especially for real-world classification problems. The major inconvenience has been attributed to the difficulty of determining the intrinsic characteristics of real-world classification problems. This paper investigates the domain of competence of XCS by means of a methodology that characterizes the complexity of a classification problem by a set of geometrical descriptors. In a study of 392 classification problems along with their complexity characterization, we are able to identify difficult and easy domains for XCS. We focus on XCS with hyperrectangle codification, which has been predominantly used for real-attributed domains. The results show high correlations between XCS's performance and measures of length of class boundaries, compactness of classes, and nonlinearities of decision boundaries. We also compare the relative performance of XCS with other traditional classifier schemes. Besides confirming the high degree of competence of XCS in these problems, we are able to relate the behavior of the different classifier schemes to the geometrical complexity of the problem. Moreover, the results highlight certain regions of the complexity measurement space where a classifier scheme excels, establishing a first step toward determining the best classifier scheme for a given classification problem.