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A Novel GA-Taguchi-Based Feature Selection Method

  • Cheng-Hong Yang
  • Chi-Chun Huang
  • Kuo-Chuan Wu
  • Hsin-Yun Chang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)

Abstract

This work presents a novel GA-Taguchi-based feature selection method. Genetic algorithms are utilized with randomness for “global search” of the entire search space of the intractable search problem. Various genetic operations, including crossover, mutation, selection and replacement are performed to assist the search procedure in escaping from sub-optimal solutions. In each iteration in the proposed nature-inspired method, the Taguchi methods are employed for “local search” of the entire search space and thus can help explore better feature subsets for next iteration. The two-level orthogonal array is utilized for a well-organized and balanced comparison of two levels for features—a feature is or is not selected for pattern classification—and interactions among features. The signal-to-noise ratio (SNR) is then used to determine the robustness of the features. As a result, feature subset evaluation efforts can be significantly reduced and a superior feature subset with high classification performance can be obtained. Experiments are performed on different application domains to demonstrate the performance of the proposed nature-inspired method. The proposed hybrid GA-Taguchi-based approach, with wrapper nature, yields superior performance and improves classification accuracy in pattern classification.

Keywords

Genetic Algorithm Taguchi Method Orthogonal Array Feature Subset Selection Pattern Classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Cheng-Hong Yang
    • 1
  • Chi-Chun Huang
    • 2
  • Kuo-Chuan Wu
    • 1
  • Hsin-Yun Chang
    • 3
    • 4
  1. 1.Department of Computer Science and Information EngineeringNational Kaohsiung University of Applied SciencesKaohsiungTaiwan
  2. 2.Department of Information ManagementNational Kaohsiung Marine UniversityKaohsiungTaiwan
  3. 3.Department of Business AdministrationChin-Min Institute of TechnologyTou-FenTaiwan
  4. 4.Department of Industrial Technology EducationNational Kaohsiung Normal UniversityKaohsiungTaiwan

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