Machine Learning
Strength or Accuracy? Fitness Calculation in Learning Classifier Systems
Learning Classifier Systems, From Foundations to Applications
IWLCS '00 Revised Papers from the Third 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
Bounding the effect of noise in multiobjective learning classifier systems
Evolutionary Computation
Strong, Stable, and Reliable Fitness Pressure in XCS due to Tournament Selection
Genetic Programming and Evolvable Machines
Learning classifier system ensemble for data mining
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Learning classifier systems: a survey
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Performance and efficiency of memetic pittsburgh learning classifier systems
Evolutionary Computation
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
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
LCSE: learning classifier system ensemble for incremental medical instances
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
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
Efficient training set use for blood pressure prediction in a large scale learning classifier system
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Effective rule generalization in learning classifier systems (LCSs) has long since been an important consideration. In noisy problem domains, where attributes do not precisely determine class, overemphasis on accuracy without sufficient generalization leads to over-fitting of the training data, and a large discrepancy between training and testing accuracies. This issue is of particular concern within noisy bioinformatic problems such as complex disease, gene association studies. In an effort to promote effective generalization we introduce and explore a simple strategy which seeks to discourage over-fitting via the probabilistic incorporation of random noise within training instances. We evaluate a variety of noise models and magnitudes which either specify an equal probability of noise per attribute, or target higher noise probability to the attributes which tend to be more frequently generalized. Our results suggest that targeted noise incorporation can reduce training accuracy without eroding testing accuracy. In addition, we observe a slight improvement in our power estimates (i.e. ability to detect the true underlying model(s)).