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Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR'04)
Unsupervised Feature Selection for Ensemble of Classifiers
Kokubunji, Tokyo, Japan
October 26-October 29
ISBN: 0-7695-2187-8
Marisa Morita, Pontifícia Universidade Católica do Paraná
Luiz S. Oliveira, Pontifícia Universidade Católica do Paraná
Robert Sabourin, Pontifícia Universidade Católica do Paraná
In this paper we discuss a strategy to create ensemble of classifiers based on unsupervised features selection. It takes into account a hierarchical multi-objective genetic algorithm that generates a set of classifiers by performing feature selection and then combines them to provide a set of powerful ensembles. The proposed method is evaluated in the context of handwritten month word recognition, using three different feature sets and Hidden Markov Models as classifiers. Comprehensive experiments demonstrates the effectiveness of the proposed strategy.
Index Terms:
Ensemble of Classifiers, Unsupervised Feature Selection, Handwriting Recognition, Multi-objective Optimization, Genetic Algorithms
Citation:
Marisa Morita, Luiz S. Oliveira, Robert Sabourin, "Unsupervised Feature Selection for Ensemble of Classifiers," iwfhr, pp.81-86, Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR'04), 2004
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