A Kind of Fuzzily Combinative Classifiers for Solving Large-Scale Learning Problems

In order to use combinative classifiers to effectively solve 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. (C) Transformation of outputs of single classifiers into the grades of membership. We select improved kernel Fisher, Mahalanobis distance, and 10-nearest-neighbor classifier, 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 membership. The experiment for letter recognition shows that the proposed method is effective