Fuzzy principal component regression (FPCR) for fuzzy input and output data
Resource
International Journal of Uncertainty Fuzziness and Knowledge-Based Systems 14 (1): 87-100
Journal
International Journal of Uncertainty Fuzziness and Knowledge-Based Systems
Journal Volume
14
Journal Issue
1
Pages
87-100
Date Issued
2006
Date
2006
Author(s)
Abstract
Although fuzzy regression is widely employed to solve many problems in practice, what seems to be lacking is the problem of multicollmearity. In this paper, the fuzzy centers principal component analysis is proposed to first derive the fuzzy principal component scores. Then the fuzzy principal component regression (FPCR) is formed to overcome the problem of multicollinearity in the fuzzy regression model. In addition, a numerical example is used to demonstrate the proposed method and compare with other methods. On the basis of the results, we can conclude that the proposed method can provide a correct fuzzy regression model and avoid the problem of multicollinearity. © World Scientific Publishing Company.
Subjects
Fuzzy centers principal component analysis; Fuzzy principal component regression (FPCR); Fuzzy principal component scores; Fuzzy regression; Multicollinearity
Other Subjects
Data reduction; Fuzzy sets; Mathematical models; Problem solving; Regression analysis; Fuzzy centers principal component analysis; Fuzzy principal component regression (FPCR); Fuzzy principal component scores; Fuzzy regression; Multicollinearity; Principal component analysis
Type
journal article
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