A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Subset Selection and Order Identification for Unsupervised Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Handwritten Month Word Recognition on Brazilian Bank Checks
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Engineering multiversion neural-net systems
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Approximate Reasoning
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
A memetic model of evolutionary PSO for computational finance applications
Expert Systems with Applications: An International Journal
International Journal of Hybrid Intelligent Systems - Hybrid Fuzzy Models
Multiobjective evolutionary neural networks for time series forecasting
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Selective neural network ensemble based on clustering
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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Feature selection for ensembles has shown to be an effective strategy for ensemble creation due to its ability of producing good subsets of features, which make the classifiers of the ensemble disagree on difficult cases. In this paper we present an ensemble feature selection approach based on a hierarchical multi-objective genetic algorithm. The algorithm operates in two levels. Firstly, it performs feature selection in order to generate a set of classifiers and then it chooses the best team of classifiers. In order to show its robustness, the method is evaluated in two different contexts: supervised and unsupervised feature selection. In the former, we have considered the problem of handwritten digit recognition while in the latter, we took into account the problem of handwritten month word recognition. Experiments and comparisons with classical methods, such as Bagging and Boosting, demonstrated that the proposed methodology brings compelling improvements when classifiers have to work with very low error rates.