Dynamic classifier ensemble using classification confidence

Abstract

How to combine the outputs from base classifiers is a key issue in ensemble learning. This paper presents a dynamic classifier ensemble method termed as DCE-CC. It dynamically selects a subset of classifiers for test samples according to classification confidence. The weights of base classifiers are learned by optimization of margin distribution on the training set, and the ordered aggregation technique is exploited to estimate the size of an appropriate subset. We examine the proposed fusion method on some benchmark classification tasks, where the stable nearest-neighbor rule and the unstable C4.5 decision tree algorithm are used for generating base classifiers, respectively. Compared with some other multiple classifier fusion algorithms, the experimental results show the effectiveness of our approach. Then we explain the experimental results from the view point of margin distribution.