A classifier ensemble model and its applications

In order to use combinative classifiers to effectively solve the large-scale learning problems, this paper focuses on the following aspects: (A) decomposition of large-scale learning problems; (B) selection of units of combinative classifiers; and (C) transformation of outputs of single classifiers into the grades of memberships. We select Gaussian kernel, 10-nearest-neighbor, and quadratic polynomial, as the combinative units, only let the most relative part of the original datasets to take part in training a single classifier, and then transform the outputs of each classifier into the same grades of memberships. The experiment for letter recognition shows that the proposed method is effective.