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Ridge Fuzzy Regression Model
- Authors
- Choi, Seung Hoe; Jung, Hye-Young; Kim, Hyoshin
- Issue Date
- Oct-2019
- Publisher
- Chinese Fuzzy Systems Association
- Keywords
- Ridge regression; Multicollinearity; Ridge fuzzy regression model; Fuzzy multiple linear regression model
- Citation
- International Journal of Fuzzy Systems, v.21, no.7, pp.2077 - 2090
- Indexed
- SCIE
SCOPUS
- Journal Title
- International Journal of Fuzzy Systems
- Volume
- 21
- Number
- 7
- Start Page
- 2077
- End Page
- 2090
- ISSN
- 1562-2479
- Abstract
- Ridge regression model is a widely used model with many successful applications, especially in managing correlated covariates in a multiple regression model. Multicollinearity represents a serious threat in fuzzy regression models as well. We address this issue by combining ridge regression with the fuzzy regression model. Our proposed algorithm uses the a-level estimation method to evaluate the parameters of the ridge fuzzy regression model. Two examples are given to illustrate the ridge fuzzy regression model with crisp input/fuzzy output and fuzzy coefficients.
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