Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Decision Trees and Neural Networks for Drug Discovery
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
An analysis of diversity measures
Machine Learning
GNeurAge: An Evolutionary Agent-Based System for Classification Tasks
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Comparison of classifier selection methods for improving committee performance
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
A new ensemble diversity measure applied to thinning ensembles
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A multiagent genetic algorithm for global numerical optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An experimental bias-variance analysis of SVM ensembles based on resampling techniques
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
Multi-objective genetic algorithm evaluation in feature selection
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
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Classifier ensembles are systems composed of a set of individual classifiers (organized in a parallel way) and a combination module, which is responsible for providing the final output of the system. In the design of these systems, diversity is considered as one of the main aspects to be taken into account, since there is no gain in combining identical classification methods. One way of increasing diversity is to provide different datasets (patterns and/or attributes) for the individual classifiers. In this context, it is envisaged to use, for instance, feature selection methods in order to select subsets of attributes for the individual classifiers. However, the majority of the papers using feature selection for ensembles address the homogenous structures of ensemble, i.e., ensembles composed only of the same type of classifiers. In this paper, two approaches of genetic algorithms (single and multi-objective) will be used to guide the distribution of the features among the classifiers in the context of heterogeneous ensembles.