Building a type-2 fuzzy regression model based on credibility theory and its application on arbitrage pricing theory

Yicheng Wei; Junzo Watada

doi:10.1109/FUZZ-IEEE.2014.6891608

Building a type-2 fuzzy regression model based on credibility theory and its application on arbitrage pricing theory

Yicheng Wei, Junzo Watada

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

1 Citation (Scopus)

Abstract

Real life circumstances used to provide us with linguistically vague expression of data in nature. Thus, type-1 fuzzy set (T1F set) was introduced to model this uncertainty. Additionally, same words will mean variously to different people, which means ambiguous uncertainty also exists when associated with the membership function of a T1F set. Type-2 fuzzy set(T2F set) is then invented to express the hybrid uncertainty of both primary fuzziness and secondary one of membership functions. On the one hand, T2F variable models the vagueness of information better. On the other hand, those variables are hard to deal with its three-dimensional feature given. To address problems in presence of such variables with hybrid fuzziness, a new class of T2F regression model is built based on credibility theory, called the T2F expected value regression model. The new model will be developed in this paper. This paper is a further work based on our former research of T2F qualitative regression model.

Original language	English
Title of host publication	IEEE International Conference on Fuzzy Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2368-2375
Number of pages	8
ISBN (Print)	9781479920723
DOIs	https://doi.org/10.1109/FUZZ-IEEE.2014.6891608
Publication status	Published - 2014 Sep 4
Event	2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 - Beijing Duration: 2014 Jul 6 → 2014 Jul 11

Other

Other	2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014
City	Beijing
Period	14/7/6 → 14/7/11

Keywords

Credibility theory
expected value
regression model
Type-2 fuzzy set

ASJC Scopus subject areas

Software
Artificial Intelligence
Applied Mathematics
Theoretical Computer Science

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Wei, Y & Watada, J 2014, Building a type-2 fuzzy regression model based on credibility theory and its application on arbitrage pricing theory. in IEEE International Conference on Fuzzy Systems., 6891608, Institute of Electrical and Electronics Engineers Inc., pp. 2368-2375, 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014, Beijing, 14/7/6. https://doi.org/10.1109/FUZZ-IEEE.2014.6891608

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abstract = "Real life circumstances used to provide us with linguistically vague expression of data in nature. Thus, type-1 fuzzy set (T1F set) was introduced to model this uncertainty. Additionally, same words will mean variously to different people, which means ambiguous uncertainty also exists when associated with the membership function of a T1F set. Type-2 fuzzy set(T2F set) is then invented to express the hybrid uncertainty of both primary fuzziness and secondary one of membership functions. On the one hand, T2F variable models the vagueness of information better. On the other hand, those variables are hard to deal with its three-dimensional feature given. To address problems in presence of such variables with hybrid fuzziness, a new class of T2F regression model is built based on credibility theory, called the T2F expected value regression model. The new model will be developed in this paper. This paper is a further work based on our former research of T2F qualitative regression model.",

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