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2008
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The implication of data diversity for a classifier-free ensemble selection in random subspaces

10 years 1 months ago
The implication of data diversity for a classifier-free ensemble selection in random subspaces
Ensemble of Classifiers (EoC) has been shown effective in improving the performance of single classifiers by combining their outputs. By using diverse data subsets to train classifiers, the ensemble creation methods can create diverse classifiers for the EoC. In this work, we propose a scheme to measure the data diversity directly from random subspaces and we explore the possibility of using the data diversity directly to select the best data subsets for the construction of the EoC. The applicability is tested on NIST SD19 handwritten numerals.
Albert Hung-Ren Ko, Robert Sabourin, Luiz E. Soare
Added 05 Nov 2009
Updated 06 Nov 2009
Type Conference
Year 2008
Where ICPR
Authors Albert Hung-Ren Ko, Robert Sabourin, Luiz E. Soares de Oliveira, Alceu de Souza Britto Jr.
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