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 | |
| 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 |
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Keywords
- Credibility theory
- expected value
- regression model
- Type-2 fuzzy set
ASJC Scopus subject areas
- Software
- Artificial Intelligence
- Applied Mathematics
- Theoretical Computer Science
Cite this
Building a type-2 fuzzy regression model based on credibility theory and its application on arbitrage pricing theory. / Wei, Yicheng; Watada, Junzo.
IEEE International Conference on Fuzzy Systems. Institute of Electrical and Electronics Engineers Inc., 2014. p. 2368-2375 6891608.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Building a type-2 fuzzy regression model based on credibility theory and its application on arbitrage pricing theory
AU - Wei, Yicheng
AU - Watada, Junzo
PY - 2014/9/4
Y1 - 2014/9/4
N2 - 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.
AB - 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.
KW - Credibility theory
KW - expected value
KW - regression model
KW - Type-2 fuzzy set
UR - http://www.scopus.com/inward/record.url?scp=84912544586&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84912544586&partnerID=8YFLogxK
U2 - 10.1109/FUZZ-IEEE.2014.6891608
DO - 10.1109/FUZZ-IEEE.2014.6891608
M3 - Conference contribution
AN - SCOPUS:84912544586
SN - 9781479920723
SP - 2368
EP - 2375
BT - IEEE International Conference on Fuzzy Systems
PB - Institute of Electrical and Electronics Engineers Inc.
ER -