Distributed representation of fuzzy rules and its application to pattern classification
Fuzzy Sets and Systems
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Parallel Evolutionary Computations (Studies in Computational Intelligence)
Parallel Evolutionary Computations (Studies in Computational Intelligence)
International Journal of Approximate Reasoning
Intrusion detection using a fuzzy genetics-based learning algorithm
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Learning Classifier Systems in Data Mining
Learning Classifier Systems in Data Mining
Parallel distributed genetic fuzzy rule selection
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A proposal on reasoning methods in fuzzy rule-based classification systems
International Journal of Approximate Reasoning
On the impact of the migration topology on the Island Model
Parallel Computing
IEEE Transactions on Evolutionary Computation
Parallel distributed implementation of genetics-based machine learning for fuzzy classifier design
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
International Journal of Approximate Reasoning
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Hybridization of fuzzy GBML approaches for pattern classification problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rule Weight Specification in Fuzzy Rule-Based Classification Systems
IEEE Transactions on Fuzzy Systems
Fuzzy control of pH using genetic algorithms
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
A dynamic island model for adaptive operator selection
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Diversity Through Multiculturality: Assessing Migrant Choice Policies in an Island Model
IEEE Transactions on Evolutionary Computation
Training Data Subdivision and Periodical Rotation in Hybrid Fuzzy Genetics-Based Machine Learning
ICMLA '11 Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops - Volume 01
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We have already proposed an island model for parallel distributed implementation of fuzzy genetics-based machine learning (GBML) algorithms. As in many other island models, a population of individuals is divided into multiple subpopulations. Each subpopulation is assigned to a different island. The main characteristic feature of our model is that training patterns are also divided into multiple training data subsets. Each subset is assigned to a different island. The assigned subset is used to train the subpopulation in each island. The assignment of the training data subsets is periodically rotated over the islands (e.g., every 100 generations). A migration operation is also periodically used. Our original intention in the use of such an island model was to decrease the computation time of fuzzy GBML algorithms. In this paper, we propose an idea of using our island model for ensemble classifier design. An ensemble classifier is constructed by choosing the best classifier in each island. Since the subpopulation at each island is evolved using a different training data subset, a different classifier may be obtained from each island to construct an ensemble classifier. This suggests a potential ability of our island model as an ensemble classifier design tool. However, the diversity of the obtained classifiers from multiple islands seems to be decreased by frequent training data subset rotation and frequent migration. In this paper, we examine the effects of training data subset rotation and migration on the performance of designed ensemble classifiers through computational experiments.