Robust Algorithms for Regression Analysis Based on Fuzzy Objective Functions

  • Tai-Ning Yang Department of Computer Science and Information Engineering, Chinese Culture University, Taipei, Taiwan.
  • Chih-Jen Lee Department of Computer Science and Information Engineering, Chinese Culture University, Taipei, Taiwan.
  • Jenn-Dong Sun Department of Computer Science and Information Engineering, Chinese Culture University, Taipei, Taiwan.
  • Chun-Jung Chen Department of Computer Science and Information Engineering, Chinese Culture University, Taipei, Taiwan.
Keywords: robust regression, fuzzy complement, linear regression analysis

Abstract

In this paper, we address the issues related to the design of fuzzy robust linear regression algorithms. The design of robust linear regression analysis has been studied in the literature of statistics for over two decades. More recently various robust regression models have been proposed for processing noisy data. We proposed a new objective function by using fuzzy complement and derive improved algorithms that can produce good regression analysis from the spoiled data set. Data set from the U.S. Department of Transportation is used to evaluate the performance of the regression algorithms.

References

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Published
2017-12-20
How to Cite
Yang, T.-N., Lee, C.-J., Sun, J.-D., & Chen, C.-J. (2017). Robust Algorithms for Regression Analysis Based on Fuzzy Objective Functions. Proceedings of Engineering and Technology Innovation, 7, 41-44. Retrieved from http://ojs.imeti.org/index.php/PETI/article/view/950
Section
Articles