The Strength of Weak Learnability
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Hierarchical fuzzy control of multivariable systems
Fuzzy Sets and Systems
Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
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
Machine Learning
The ``Test and Select'' Approach to Ensemble Combination
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A hierarchical knowledge-based environment for linguistic modeling: models and iterative methodology
Fuzzy Sets and Systems - Theme: Learning and modeling
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)
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
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
Classifier subset selection for biomedical named entity recognition
Applied Intelligence
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
Naïve Bayes ensembles with a random oracle
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Combining bagging and random subspaces to create better ensembles
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
On the construction of hierarchical fuzzy systems models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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
"Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting
IEEE Transactions on Fuzzy Systems
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
How large should ensembles of classifiers be?
Pattern Recognition
| Hi-index | 0.00 |
Fuzzy rule-based systems have shown a high capability of knowledge extraction and representation when modeling complex, non-linear classification problems. However, they suffer from the so-called curse of dimensionality when applied to high dimensional datasets, which consist of a large number of variables and/or examples. Multiclassification systems have shown to be a good approach to deal with this kind of problems. In this contribution, we propose an multiclassification system-based global framework allowing fuzzy rule-based systems to deal with high dimensional datasets avoiding the curse of dimensionality. Having this goal in mind, the proposed framework will incorporate several multiclassification system methodologies as well as evolutionary algorithms to design fuzzy rule-based multiclassification systems. The proposed framework follows a two-stage structure: 1) fuzzy rule-based multiclassification system design from classical and advanced multiclassification system design approaches, and 2) novel designs of evolutionary component classifier combination. By using our methodology, different fuzzy rule-based multiclassification systems can be designed dealing with several aspects such as improvement of the performance in terms of accuracy, and obtaining a good accuracy-complexity trade-off.