Abstract
In this paper we investigate the effect of data partition in constructing fuzzy ensemble classifiers. In the standard version of ensemble methods, the confidence of a base classifier is calculated based on the classification performance of the base classifier on training patterns that are used to construct it. In the proposed method we divide the training data set into two subsets. One subset is used to construct a base classifier and the other is used to calculate its confidence. In the computational experiments in this paper we compare the performance of the proposed method with that of well-known classifiers. The experimental results show that the proposed method is effective for improving the performance of the fuzzy ensemble classifiers.
