Deformation Decision Knowledge Extraction of FWP Processing Based on RS and Entropy

  • Yaohua Deng
  • Guixiong Liu
  • Liming Wu
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 146)

Abstract

Deformation decisions knowledge extraction based on RS and entropy reduction is proposed for FWP processing which processing deformation factors is complex and difficult to extract. Decision table of processing deformation which takes FWP processing deformation factors as conditional attribute and processing deformation level as decision attribute. Changes in the level of mutual information as a condition attribute importance to the decision attribute evaluation, when the greater the change in the conditions the more important attribute for decision attribute. In Application examples, there are 13 conditional attribute in decision table, in accordance with Pawlak reduction methods and genetic reduction algorithm to extract 5 highest impact factors, 4 highest impact factors for FWP processing deformation decision are extracted using entropy reduction method. Prediction error of Entropy Reduction Method is 32.58% less than Pawlak’s reduction method, and 21.45% less than Genetic reduction algorithm.

Keywords

FWP processing deformation decisions knowledge extraction rough set information entropy attribute importance 

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Yaohua Deng
    • 1
    • 2
  • Guixiong Liu
    • 1
  • Liming Wu
    • 2
  1. 1.School of Mechanical and Automotive EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.School of Information EngineeringGuangdong University of TechnologyGuangzhouChina

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