Adaptive fusion and co-operative training for classifier ensembles

In this paper, architectures and methods of decision aggregation in classifier ensembles are investigated. Typically, ensembles are designed in such a way that each classifier is trained independently and the decision fusion is performed as a post-process module. In this study, however, we are interested in making the fusion a more adaptive process. We first propose a new architecture that utilizes the features of a problem to guide the decision fusion process. By using both the features and classifiers outputs, the recognition strengths and weaknesses of the different classifiers are identified. This information is used to improve overall generalization capability of the system. Furthermore, we propose a co-operative training algorithm that allows the final classification to determine whether further training should be carried out on the components of the architecture. The performance of the proposed architecture is assessed by testing it on several benchmark problems. The new architecture shows improvement over existing aggregation techniques. Moreover, the proposed co-operative training algorithm provides a means to limit the users' intervention, and maintains a level of accuracy that is competitive to that of most other approaches.