This paper proposes an innovative combinational algorithm to improve the performance in multiclass classification domains. Because the more accurate classifier the better performance of classification, the researchers in computer communities have been tended to improve the accuracies of classifiers. Although obtaining the more accurate classifier is often aimed, there is an alternative option to reach for it. Indeed one can use many inaccurate classifiers each of which is specialized for a subspace 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 binary classifiers as elements of an ensemble. These ensembles of binary 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.