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A Novel Ensemble Approach for Improving Generalization Ability of Neural Networks

  • Lei Lu
  • Xiaoqin Zeng
  • Shengli Wu
  • Shuiming Zhong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)

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.

Keywords

Ensemble learning diversity sensitivity fusion clustering the second training 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Lei Lu
    • 1
  • Xiaoqin Zeng
    • 1
  • Shengli Wu
    • 2
  • Shuiming Zhong
    • 1
  1. 1.Department of Computer Science and EngineeringHohai UniversityNanjingChina
  2. 2.School of Computer and MathematicsUniversity of UlsterUK

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