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A Novel Hybrid Taguchi-Grey-Based Method for Feature Subset Selection

  • Hsin-Yun Chang
  • Chung-Shan Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

In this paper, a novel hybrid Taguchi-Grey-based method for feature subset selection is proposed. The two-level orthogonal array is employed in the proposed method to provide a well-organized and balanced comparison of two levels of each feature (i.e., the feature is selected for pattern classification or not) and interactions among all features in a specific classification problem. That is, this two-dimensional matrix is mainly used to reduce the feature subset evaluation efforts prior to the classification procedure. Accordingly, the grey-based nearest neighbor rule and the signal-to-noise ratio (SNR) are used to evaluate and optimize the features of the specific classification problem. In this manner, important and relevant features can be identified for pattern classification. Experiments performed on different application domains are reported to demonstrate the performance of the proposed hybrid Taguchi-Grey-based method. It can be easily seen that the proposed method yields superior performance and is helpful for improving the classification accuracy in pattern classification.

Keywords

Feature Subset Selection Taguchi Methods Grey-based Nearest Neighbor Rule Pattern Classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Hsin-Yun Chang
    • 1
  • Chung-Shan Sun
    • 2
  1. 1.Department of Business Administration, Chin-Min Institute of Technology, 110 Hsueh-Fu Road, Tou-Fen, Miao-Li 305, Taiwan, Department of Industrial Technology Education, National Kaohsiung Normal University, 116 Heping 1st RD., Lingya District, Kaohsiung 802Taiwan
  2. 2.Department of Industrial Technology Education, National Kaohsiung Normal University, 116 Heping 1st RD., Lingya District, Kaohsiung 802Taiwan

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