single-jc.php

JACIII Vol.15 No.4 pp. 479-484
doi: 10.20965/jaciii.2011.p0479
(2011)

Paper:

Fuzzy Robust Regression Model by Possibility Maximization

Yoshiyuki Yabuuchi* and Junzo Watada**

*Faculty of Economics, Shimonoseki City University, 2-1-1 Daigaku-cho, Shimonoseki, Yamaguchi 751-8510, Japan

**Graduate School of Information, Production and Systems Waseda University, 2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka 808-0196, Japan

Received:
January 7, 2011
Accepted:
February 25, 2011
Published:
June 20, 2011
Keywords:
fuzzy regression model, possibility grade, robustness
Abstract
Since management and economic systems are complex, it is hard to handle data obtained in management and economic areas. Hitherto, in the fields, much research has focused on the structure and analysis of such data. H. Tanaka et al. proposed a fuzzy regression model to illustrate the potential possibilities inherent in the target system. J. C. Bezdek proposed a switching regression model based on a fuzzy clustering model to separate mixed samples coming from plural latent systems and apply regression models to the groups of samples coming from each system. It is hard to illustrate a rough and moderate possibility of the target system. In this paper, to deal with the possibility of a social system, we propose a new fuzzy robust regression model.
Cite this article as:
Y. Yabuuchi and J. Watada, “Fuzzy Robust Regression Model by Possibility Maximization,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.4, pp. 479-484, 2011.
Data files:
References
  1. [1] H. Tanaka and J. Watada, “Possibilistic Linear Systems and Their Application to The Linear Regression Model,” Fuzzy Sets and Systems, Vol.27, pp. 275-289, 1988.
  2. [2] H. Tanaka and P. Guo, “Possibilistic Data Analysis for Operations Research,” Phisica-Verlag, 1999.
  3. [3] H. Ishibuchi and H. Tanaka, “Interval Regression Analysis by Mixed 0-1 Integer Programming Problem,” J. of Japanese Industrial Management Association, Vol.40, No.5, pp. 312-319, 1988. (in Japanese)
  4. [4] Y. Yabuuchi and J. Watada, “Fuzzy Robust Regression Analysis based on A Hyperelliptic Function,” Proc. of the 4th IEEE Int. Conf. on Fuzzy Systems, pp. 1841-1848, 1995.
  5. [5] H. Lee and H. Tanaka, “Dealing with Outliers by Fuzzy Regression Reflecting Central Tendency,” Proc. of the 13th Fuzzy System Symposium, pp. 365-366, 1997. (in Japanese)
  6. [6] M. Inuiguchi, M. Sakawa, and S. Ushiro, “Median Fuzzy Linear Regression,” Proc. of the 13th Fuzzy System Symposium, pp. 367-368, 1997. (in Japanese)
  7. [7] H. Tajima, “A Proposal of Fuzzy Regression Model,” Proc. of The Vietnam-Japan Bilateral Symposium Fuzzy Systems and Applications, pp. 383-389, 1998.
  8. [8] Y. Yabuuchi and J.Watada, “Model Building Based on Central Position for a Fuzzy Regression Model,” Proc. of Czech-Japan Seminar 2006, pp. 114-119, 2006.
  9. [9] Y. Yabuuchi and J. Watada, “Fuzzy Regression Model Building through Possibility Maximization and Its Application,” Innovative Computing, Information and Control Express Letters, Vol.4, No.2, pp. 505-510, 2010.
  10. [10] T. Hasuike, H. Katagiri, and H. Ishii, “Multiobjective Random Fuzzy Linear Programming Problems Based on the Possibility Maximization Model,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.13, No.4 pp. 373-379, 2009.
  11. [11] A. Honda and Y. Okazaki, “Identification of Fuzzy Measures with Distorted Probability Measures,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.9, No.5 pp. 467-476, 2005.
  12. [12] “Water Handbook Editorial Committee,” Water Handbook, p. 95, Maruzen, 2003. (in Japanese)

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 05, 2024