An evolutionary modular neural network for unbalanced pattern classifications

In this paper, an evolutionary modular neural network is proposed to solve multi-class problems with unbalanced training sets. The proposed model can transform an unbalanced classification problem into a set of symmetrical two-class problems, each of which can be solved by a single simple neural network. The experimental results show that the proposed method reduces time consumption for training and improves the classification performance.