Fuzzy robust regression models based on granularity and possibility distribution

Yoshiyuki Yabuuchi; Junzo Watada

doi:10.1109/SCIS-ISIS.2014.7044751

Fuzzy robust regression models based on granularity and possibility distribution

Yoshiyuki Yabuuchi, Junzo Watada

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The characteristic of the fuzzy regression model is to enwrap all the given samples. An interval of fuzzy regression model is created by considering how far a sample is from the central values. That means when samples are widely scattered the size of an interval of the fuzzy model is widened. That is, the fuzziness of the fuzzy regression model is decided by the range of sample distribution. Therefore, many research results on a fuzzy regression model in order to describe the possibility of the target system have been reported. We have proposed two fuzzy robust regression models which remove influences of improper data such as unusual data and outliers. In this paper, we describe the model building of our fuzzy robust regressions by removing influences of improper data.

Original language	English
Title of host publication	2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1386-1391
Number of pages	6
ISBN (Print)	9781479959556
DOIs	https://doi.org/10.1109/SCIS-ISIS.2014.7044751
Publication status	Published - 2014 Feb 18
Event	2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 - Kitakyushu, Japan Duration: 2014 Dec 3 → 2014 Dec 6

Other

Other	2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014
Country/Territory	Japan
City	Kitakyushu
Period	14/12/3 → 14/12/6

ASJC Scopus subject areas

Software
Artificial Intelligence

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10.1109/SCIS-ISIS.2014.7044751

Cite this

Yabuuchi, Y., & Watada, J. (2014). Fuzzy robust regression models based on granularity and possibility distribution. In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 (pp. 1386-1391). [7044751] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SCIS-ISIS.2014.7044751

Fuzzy robust regression models based on granularity and possibility distribution. / Yabuuchi, Yoshiyuki; Watada, Junzo.

2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1386-1391 7044751.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yabuuchi, Y & Watada, J 2014, Fuzzy robust regression models based on granularity and possibility distribution. in 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014., 7044751, Institute of Electrical and Electronics Engineers Inc., pp. 1386-1391, 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014, Kitakyushu, Japan, 14/12/3. https://doi.org/10.1109/SCIS-ISIS.2014.7044751

Yabuuchi Y, Watada J. Fuzzy robust regression models based on granularity and possibility distribution. In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1386-1391. 7044751 doi: 10.1109/SCIS-ISIS.2014.7044751

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Fuzzy robust regression models based on granularity and possibility distribution

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