A Novel Ensemble Approach for Improving Generalization Ability of Neural Networks

Authors:
Lei Lu;Xiaoqin Zeng;Shengli Wu;Shuiming Zhong
Affiliations:
Department of Computer Science and Engineering, Hohai University, Nanjing, China;Department of Computer Science and Engineering, Hohai University, Nanjing, China;School of Computer and Mathematics, University of Ulster, UK;Department of Computer Science and Engineering, Hohai University, Nanjing, China
Venue:
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Year:
2008

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Abstract

Ensemble learning is one of the main directions in machine learning and data mining, which allows learners to achieve higher training accuracy and better generalization ability. In this paper, with an aim at improving generalization performance, a novel approach to construct an ensemble of neural networks is proposed. The main contributions of the approach are its diversity measure for selecting diverse individual neural networks and weighted fusion technique for assigning proper weights to the selected individuals. Experimental results demonstrate that the proposed approach is effective.