C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Classifiers that approximate functions
Natural Computing: an international journal
Evolutionary Computation
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
Strong, Stable, and Reliable Fitness Pressure in XCS due to Tournament Selection
Genetic Programming and Evolvable Machines
Bounding XCS's parameters for unbalanced datasets
Proceedings of the 8th annual conference on Genetic and evolutionary computation
UCSpv: principled voting in UCS rule populations
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
Effect of pure error-based fitness in XCS
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
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
Accuracy exponentiation in UCS and its effect on voting margins
Proceedings of the 13th annual conference 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
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This paper provides a deep insight into the learning mechanisms of UCS, a learning classifier system (LCS) derived from XCS that works under a supervised learning scheme. A complete description of the system is given with the aim of being useful as an implementation guide. Besides, we review the fitness computation, based on the individual accuracy of each rule, and introduce a fitness sharing scheme to UCS. We analyze the dynamics of UCS both with fitness sharing and without fitness sharing over five binary-input problems widely used in the LCSs framework. Also XCS is included in the comparison to analyze the differences in behavior between both systems. Results show the benefits of fitness sharing in all the tested problems, specially those with class imbalances. Comparison with XCS highlights the dynamics differences between both systems.