A New Classifiers Ensemble Method for Handwritten Pen Digits Classification
Journal Title: International Research Journal of Applied and Basic Sciences - Year 2013, Vol 5, Issue 9
Abstract
Recent researches have shown that ensembles of classifiers have more accuracy than a single classifier. Baging, boosting and error correcting output codes (ECOC) are most common ways for creating combination of classifiers. In this paper a new method for ensemble of classifiers has been introduced and performance of this method examined by applying to handwritten pen digits dataset. Experimental results indicate that this method leads to more accurate classification than other existing methods.
Authors and Affiliations
M. Hassanzadeh| Faculty of electrical and computer engineering, Babol University of Technology, Babol, Iran, email: m.hasanzadeh@nit.ac.ir, G. Ardeshir| Faculty of electrical and computer engineering, Babol University of Technology, Babol, Iran
Keywords
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