A NEW CLASSIFIERS ENSEMBLE METHOD FOR HANDWRITTEN PEN DIGITS CLASSIFICATION
Maryam Hassanzadeh, Gholamreza Ardeshir · 2013
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.