A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases
Learning Classifier Systems, From Foundations to Applications
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
Automated alphabet reduction method with evolutionary algorithms for protein structure prediction
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
Bioinformatics
Short communication: Mining knowledge from data using Anticipatory Classifier System
Knowledge-Based Systems
Prediction of topological contacts in proteins using learning classifier systems
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
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Tournament selection: stable fitness pressure in XCS
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
LCSE: learning classifier system ensemble for incremental medical instances
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Data mining in learning classifier systems: comparing XCS with GAssist
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Rule discovery in epidemiologic surveillance data using EpiXCS: an evolutionary computation approach
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Random artificial incorporation of noise in a learning classifier system environment
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Proceedings of the 14th annual conference on Genetic and evolutionary computation
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
A simple multi-core parallelization strategy for learning classifier system evaluation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Genetic epidemiologists, tasked with the disentanglement of genotype-to-phenotype mappings, continue to struggle with a variety of phenomena which obscure the underlying etiologies of common complex diseases. For genetic association studies, genetic heterogeneity (GH) and epistasis (gene-gene interactions) epitomize well recognized phenomenon which represent a difficult, but accessible challenge for computational biologists. While progress has been made addressing epistasis, methods for dealing with GH tend to "side-step" the problem, limited by a dependence on potentially arbitrary cutoffs/covariates, and a loss in power synonymous with data stratification. In the present study, we explore an alternative strategy (Learning Classifier Systems (LCSs)) as a direct approach for the characterization, and modeling of disease in the presence of both GH and epistasis. This evaluation involves (1) implementing standardized versions of existing Michigan-Style LCSs (XCS, MCS, and UCS), (2) examining major run parameters, and (3) performing quantitative and qualitative evaluations across a spectrum of simulated datasets. The results of this study highlight the strengths and weaknesses of the Michigan LCS architectures examined, providing proof of principle for the application of LCSs to the GH/epistasis problem, and laying the foundation for the development of an LCS algorithm specifically designed to address GH.