Neurocomputing: foundations of research
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Class imbalances versus small disjuncts
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Strong, Stable, and Reliable Fitness Pressure in XCS due to Tournament Selection
Genetic Programming and Evolvable Machines
The class imbalance problem in learning classifier systems: a preliminary study
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Bounding XCS's parameters for unbalanced datasets
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines
Modeling selection pressure in XCS for proportionate and tournament selection
Proceedings of the 9th annual conference on Genetic and evolutionary computation
The class imbalance problem: A systematic study
Intelligent Data Analysis
Classifier fitness based on accuracy
Evolutionary Computation
Learning Classifier Systems in Data Mining
Learning Classifier Systems in Data Mining
PRIE: a system for generating rulelists to maximize ROC performance
Data Mining and Knowledge Discovery
Evolutionary rule-based systems for imbalanced data sets
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
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
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
How XCS deals with rarities in domains with continuous attributes
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Analysing bioHEL using challenging boolean functions
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets
Pattern Recognition Letters
XCSF with local deletion: preventing detrimental forgetting
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Self-adaptation of learning rate in XCS working in noisy and dynamic environments
Computers in Human Behavior
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
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
Resource management and scalability of the XCSF learning classifier system
Theoretical Computer Science
Learning local linear Jacobians for flexible and adaptive robot arm control
Genetic Programming and Evolvable Machines
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
Engineering Applications of Artificial Intelligence
Information Sciences: an International Journal
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Michigan-style learning classifier systems (LCSs) are online machine learning techniques that incrementally evolve distributed subsolutions which individually solve a portion of the problem space. As in many machine learning systems, extracting accurate models from problems with class imbalances--that is, problems in which one of the classes is poorly represented with respect to the other classes--has been identified as a key challenge to LCSs. Empirical studies have shown that Michigan-style LCSs fail to provide accurate subsolutions that represent the minority class in domains with moderate and large disproportion of examples per class; however, the causes of this failure have not been analyzed in detail. Therefore, the aim of this paper is to carefully examine the effect of class imbalances on different LCS components. The analysis focuses on XCS, which is the most-relevant Michigan-style LCS, although the models could be easily adapted to other LCSs. Design decomposition is used to identify five elements that are crucial to guaranteeing the success of LCSs in domains with class imbalances, and facetwise models that explain these different elements for XCS are developed. All theoretical models are validated with artificial problems. The integration of all these models enables us to identify the sweet spot where XCS is able to scalably and efficiently evolve accurate models of rare classes; furthermore, facetwise analysis is used as a tool for designing a set of configuration guidelines that have to be followed to ensure convergence. When properly configured, XCS is shown to be able to solve highly unbalanced problems that previously eluded solution.