caa a22 4500 46788420X CHVBK 20180406152729.0 cr unu---uuuuu 170328e20060901xx s 000 0 eng 10.1007/s10032-005-0013-6 doi (NATIONALLICENCE)springer-10.1007/s10032-005-0013-6 Feature selection for ensembles applied to handwriting recognition [Elektronische Daten] [Luiz Oliveira, Marisa Morita, Robert Sabourin] 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 underpinning paradigm is the "overproduce and choose”. 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 and used three different feature sets and multi-layer perceptron neural networks as classifiers. In the latter, we took into account the problem of handwritten month word recognition and used three different feature sets and hidden Markov models as classifiers. 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. Comparisons have been done by considering the recognition rates only. Springer-Verlag, 2006 Ensemble of classifiers nationallicence Feature selection nationallicence Handwriting recognition nationallicence Multi-objective optimization nationallicence Genetic algorithms nationallicence Oliveira Luiz Pontíficia Universidade Católica do Paraná (PUCPR), Programa de Póos-Graduação em Inform´tica Aplicada (PPGIA), Rua Imaculada Conceição 1155,Prado Velho, 80215-901, Curitiba, Pr, Brazil aut Morita Marisa École de Technologie Supérieure (ETS), Laboratoire d'Imagerie, de Vison et d'Intelligence Artificielle 1100,rue Notre Dame Ouest, H3C 1K3, Montreal, Canada aut Sabourin Robert École de Technologie Supérieure (ETS), Laboratoire d'Imagerie, de Vison et d'Intelligence Artificielle 1100,rue Notre Dame Ouest, H3C 1K3, Montreal, Canada aut International Journal of Document Analysis and Recognition (IJDAR) Springer-Verlag 8/4(2006-09-01), 262-279 1433-2833 8:4<262 2006 8 10032 https://doi.org/10.1007/s10032-005-0013-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10032-005-0013-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Oliveira Luiz Pontíficia Universidade Católica do Paraná (PUCPR), Programa de Póos-Graduação em Inform´tica Aplicada (PPGIA), Rua Imaculada Conceição 1155,Prado Velho, 80215-901, Curitiba, Pr, Brazil aut NATIONALLICENCE 700 1- Morita Marisa École de Technologie Supérieure (ETS), Laboratoire d'Imagerie, de Vison et d'Intelligence Artificielle 1100,rue Notre Dame Ouest, H3C 1K3, Montreal, Canada aut NATIONALLICENCE 700 1- Sabourin Robert École de Technologie Supérieure (ETS), Laboratoire d'Imagerie, de Vison et d'Intelligence Artificielle 1100,rue Notre Dame Ouest, H3C 1K3, Montreal, Canada aut NATIONALLICENCE 773 0- International Journal of Document Analysis and Recognition (IJDAR) Springer-Verlag 8/4(2006-09-01), 262-279 1433-2833 8:4<262 2006 8 10032 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer