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Effectively Extracting Rules from Trained Neural Networks Based on the New Measurement Method of the Classification Power of Attributes

  • Dexian Zhang
  • Yang Liu
  • Ziqiang Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3614)

Abstract

The major problems of currently used approaches for extracting symbolic rules from trained neural networks are analyzed. The lack of efficient heuristic information is the fundamental reason that causes the low effectiveness of currently used approaches. In this paper, a new measurement method of the classification power of attributes on the basis of differential information of the trained neural networks is proposed, which is suitable for both continuous attributes and discrete attributes. Based on this new measurement method, a new approach for rule extraction from trained neural networks and classification problems with continuous attributes is proposed. The performance of the new approach is demonstrated by several computing cases. The experimental results prove that the approach proposed can improve the validity of the extracted rules remarkably compared with other rule extracting approaches, especially for the complicated classification problems.

Keywords

Continuous Attribute Heuristic Information Trained Neural Network Discrete Attribute Symbolic Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dexian Zhang
    • 1
  • Yang Liu
    • 2
  • Ziqiang Wang
    • 1
  1. 1.School of Information Science and EngineeringHenan University of TechnologyZheng ZhouChina
  2. 2.Computer CollegeNorthwestern Polytecnical UniversityXi’anP.R.C(China)

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