IEEE Transactions on Pattern Analysis and Machine Intelligence
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 Selection Using Multi-Objective Genetic Algorithms for Handwritten Digit Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Creation of Classifier Ensembles for Handwritten Word Recognition Using Feature Selection Algorithms
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Classifier ensemble selection using hybrid genetic algorithms
Pattern Recognition Letters
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
Multi-objective Feature Selection with NSGA II
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Off-line cursive script recognition: current advances, comparisons and remaining problems
Artificial Intelligence Review
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Feature selection for ensembles has shown to be an effectivestrategy for ensemble creation. In this paper we presentan ensemble feature selection approach based on a hierarchicalmulti-objective genetic algorithm. The first level performsfeature selection in order to generate a set of goodclassifiers while the second one combines them to providea set of powerful ensembles. The proposed method is evaluatedin the context of handwritten digit recognition, usingthree different feature sets and neural networks (MLP) asclassifiers. Experiments conducted on NIST SD19 demonstratedthe effectiveness of the proposed strategy.