Fusion of Classifiers Based on Centrality Measures

This paper presents the Centrality Based Fusion (CBF) method for ensemble fusion which is based on the centrality measures in the context of complex network theory. Such a concept has been applied in Social Network Analysis to measure the importance of each person inside of a social network. We hypothesized that the centrality of each classifier inside of an ensemble represented as a complex network could be combined with accuracy to provide the weight for its decision during the ensemble fusion. The main idea is to derive the weight considering the classifier importance inside the ensemble network which reflects the classifiers' diversity. A robust experimental protocol based on 30 datasets has confirmed that the notion of prominence provided employing centrality measures is a promising strategy to weight the classifiers of an ensemble. When compared with 9 fusion methods of the literature, the proposed fusion method won in 189 out of 270 experiments (70%), lost in 61 cases (22.59%) and tied in 20 cases (7.41%).