Proceedings of the seventh international conference (1990) on Machine learning
Instance-Based Learning Algorithms
Machine Learning
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Evolutionary Computation
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Strength or Accuracy? Fitness Calculation in Learning Classifier Systems
Learning Classifier Systems, From Foundations to Applications
An Algorithmic Description of XCS
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
What Makes a Problem Hard for XCS?
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Modeling XCS in class imbalances: population size and parameter settings
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Mining breast cancer data with XCS
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
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
KEEL: a software tool to assess evolutionary algorithms for data mining problems
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
IEEE Transactions on Knowledge and Data Engineering
Fuzzy-UCS: a Michigan-style learning fuzzy-classifier system for supervised learning
IEEE Transactions on Evolutionary Computation
Facetwise analysis of XCS for problems with class imbalances
IEEE Transactions on Evolutionary Computation
Data mining in learning classifier systems: comparing XCS with GAssist
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
XCS for personalizing desktop interfaces
IEEE Transactions on Evolutionary Computation
Towards Understanding How Personality, Motivation, and Events Trigger Web User Activity
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Sparse episode identification in environmental datasets: The case of air quality assessment
Expert Systems with Applications: An International Journal
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Rule discovery in epidemiologic surveillance data using EpiXCS: an evolutionary computation approach
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
Learning Classifier System Ensembles With Rule-Sharing
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
Learning classifier systems (LCS) are machine learning systems designed to work for both multi-step and single-step decision tasks. The latter case presents an interesting challenge for such algorithms, especially when they are applied to real-world data mining (DM) problems. The present investigation departs from the popular approach of applying accuracy-based LCS to single-step classification and aims to uncover the potential of strength-based LCS in such tasks. Although the latter family of algorithms have often been associated with poor generalization and performance, we aim at alleviating these problems by defining appropriate extensions to the traditional strength-based LCS framework. These extensions are detailed and their effect on system performance is studied through the application of the proposed algorithm on a set of artificial problems, designed to challenge its scalability and generalization abilities. The comparison of the proposed algorithm with UCS, its state-of-the-art accuracy-based counterpart, emphasizes the effects of our extended strength-based approach and validates its competitiveness in multi-class problems with various class distributions. Overall, our work presents an investigation of strength-based LCS in the domain of supervised classification. Our extensive analysis of the learning dynamics involved in these systems provides proof of their potential as real-world DM tools, inducing tractable rule-based classification models, even in the presence of severe class imbalances.