The Strength of Weak Learnability
Machine Learning
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Essentials of Fuzzy Modeling and Control
Essentials of Fuzzy Modeling and Control
Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining (Advanced Information Processing)
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Decision Tree Ensemble Creation Techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Classifier Ensembles with a Random Linear Oracle
IEEE Transactions on Knowledge and Data Engineering
Engineering multiversion neural-net systems
Neural Computation
Evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers
International Journal of Hybrid Intelligent Systems - Hybrid Intelligent systems in Ensembles
Genetic rule selection with a multi-classifier coding scheme for ensemble classifier design
International Journal of Hybrid Intelligent Systems - Hybridization of Intelligent Systems
Looking for a good fuzzy system interpretability index: An experimental approach
International Journal of Approximate Reasoning
A proposal on reasoning methods in fuzzy rule-based classification systems
International Journal of Approximate Reasoning
International Journal of Hybrid Intelligent Systems - Hybrid Fuzzy Models
Information Sciences: an International Journal
Naïve Bayes ensembles with a random oracle
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Multi-objective genetic algorithms to create ensemble of classifiers
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Making use of population information in evolutionary artificialneural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ensembling local learners ThroughMultimodal perturbation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Recently we proposed the use of the Random Linear Oracles classical classifier ensemble (CE) design methodology in a fuzzy environment. It derived fuzzy rule-based CEs obtaining an outstanding performance. Random Oracles introduce an additional diversity into the base classifiers improving the accuracy of the entire CE. Meanwhile, the overproduce-and-choose strategy leads to a good accuracy-complexity trade-off. It is based on the generation of a large number of component classifiers and a subsequent selection of the best cooperating subset of them. The current contribution has a twofold aim: (1) Introduce a new Random Oracles approach into the fuzzy rule-based CEs design; (2) Incorporate an evolutionary multi-objective overproduce-and-choose strategy to our approach analyzing the influence of this additional diversity in the final CE performance (focusing on the accuracy). To do so, firstly, we incorporate the two Random Oracle variants into the fuzzy rule-based CE framework. Then, we use NSGA-II to provide a specific component classifier selection driven by three different criteria. Exhaustive experiments are carried out over 29 UCI and KEEL datasets with high complexity (considering both the number of attributes as well as the number of examples) showing the good performance of the proposed approach.