Abstract: This paper proposes an innovative combinational algorithm to improve the performance of multiclass problems. Because the more accurate classifier the better performance of classification, so researchers have been tended to improve the accuracies of classifiers. Although obtaining the more accurate classifier is often targeted, there is an alternative way to reach for it. Indeed one can use many inaccurate classifiers each of which is specialized for a few dataitems in the problem space and then s/he can consider their consensus vote as the classification. This paper proposes a new ensembles methodology that uses ensemble of classifiers as elements of ensemble. These ensembles of classifiers jointly work using majority weighted voting. The results of these ensembles are in weighted manner combined to decide the final vote of the classification. In empirical result, these weights in final classifier are determined with using a series of genetic algorithms. We evaluate the proposed framework on a very large scale Persian digit handwritten dataset and the results show effectiveness of the algorithm.

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{{Explor lien
   |wiki=    Ticri/CIDE
   |area=    OcrV1
   |flux=    Main
   |étape=   Merge
   |type=    RBID
   |clé=     ISTEX:F5BDF9329E000143A56F376B7967E4756F6BC1A0
   |texte=   A New Adaptive Framework for Classifier Ensemble in Multiclass Large Data
}}