A novel modular neural network for imbalanced classification problems

作者：Zhao Zhong Qiu^*

来源：Pattern Recognition Letters, 2009, 30(9): 783-788.

DOI：10.1016/j.patrec.2008.06.002

• Modular neural networks
• Imbalanced classification
• Time consumption
• Classification performance

In this paper, a novel modular neural network is proposed to solve multi-class problems with imbalanced training sets. The proposed model can transform an imbalanced classification problem into a set of symmetrical two-class problems, each of which is solved by single neural network with a simple structure. The results of all neural networks are then combined by averaging or GA method to form a final classification decision. The experimental results show that the proposed method reduces the time consumption for training and improves the classification performance.

• 出版日期2009-7-1
• 单位中国科学院; 合肥工业大学