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A Two-Stage Reduction Method Based on Rough Set and Factor Analysis

  • Zheng Liu
  • Liying Fang
  • Mingwei Yu
  • Pu Wang
  • Jianzhuo Yan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)

Abstract

The performance of dimensionality reduction on multiple, relevant, and uncertain data is not satisfied by a single method. Therefore, in this paper, we proposed a two-stage reduction method based on rough set and factor analysis (RSFA). This method integrates the advantages of feature selection on treating relevant and the advantages of rough set (RS) reduction on maintaining classification power. At first, a RS reduction is used to remove superfluous and interferential attributes. Next, a factor analysis is utilized to extract common factors to replace multi-dimension attributes. Finally, the RSFA is verified by using traditional Chinese medical clinical data to predict patients’ syndrome. The result shows that less attributes and more accuracy can be expected with RSFA, which is an appropriate reduction method for such problems.

Keywords

reduction reduction factor analysis classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zheng Liu
    • 1
  • Liying Fang
    • 1
  • Mingwei Yu
    • 2
  • Pu Wang
    • 1
  • Jianzhuo Yan
    • 1
  1. 1.Beijing University of TechnologyBeijingChina
  2. 2.Beijing Hospital of Traditional Chinese MedicineBeijingChina

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