Instance-Based Learning Algorithms
Machine Learning
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Inductive learning of decision rules from attribute-based examples: a knowledge-intensive genetic algorithm approach
C4.5: programs for machine learning
C4.5: programs for machine learning
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Rule-based evolutionary online learning systems: learning bounds, classification, and prediction
Rule-based evolutionary online learning systems: learning bounds, classification, and prediction
Classifier fitness based on accuracy
Evolutionary Computation
Effects of code growth and parsimony pressure on populations in genetic programming
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
Improving the performance of a pittsburgh learning classifier system using a default rule
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Evolutionary learning of hierarchical decision rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Coordination number prediction using learning classifier systems: performance and interpretability
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Smart crossover operator with multiple parents for a Pittsburgh learning classifier system
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System
Learning Classifier Systems
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
Proceedings of the 13th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
This paper is focused on studying the initialization stage of learning classifier systems (LCS) applying the Pittsburgh approach. It has a theoretical part where the covering probability of a random rule set is modelled and a practical part. The practical part has the objective of developing general initialization policies that have competent performance on a broad range of datasets. Two kinds of policies are tested: (1) ways of tuning the initialization probability of the system and (2) smart initialization operators that create rules that are generalized versions of randomly sampled training instances. The results identify a subset of settings that are robust enough to be considered candidates to be the default initialization policy. These settings have competent performance compared to several alternative machine learning systems. Beside identifying the good policies, the experimentation made is also useful to give hints about what kind of initial solutions is the system able to process successfully to create well generalized solutions.