A Fuzzy Support Vector Machine with Qualitative Regression Preset

Wei, Yicheng; Watada, Junzo; Pedrycz, Witold

doi:10.1109/icgec.2012.15

Cited by 5 publications

(1 citation statement)

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“…However, with regard to the complexity of type-2 fuzzy variables, there are only a few mathematical algorithms modeling, learning T2 fuzzy inputs, and predicting T2 fuzzy outputs. Recently, Wei and Watada developed a T2F qualitative regression model [33][34][35]. However, their model only applies type-2 fuzzy variables as coefficients of the system but not inputs and outputs.…”

Section: Introductionmentioning

confidence: 99%

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

Wei

Watada

2016

IEEJ Transactions Elec Engng

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Type-2 (T2) fuzzy set was introduced to model vagueness associated with primary membership function of type-1 (T1) fuzzy set. While it was invented to handle more fuzzy information, there are only a few algorithms (models) to deal with data in the form of T2 fuzzy variables given their three-dimensional features. To solve the problem, we define the expected value of a T2 fuzzy variable using credibility theory in this paper. And by substituting the expected value for the original T2 fuzzy set, the vertical uncertainties of data are transferred to horizontal ones without much distortion of information. Calculations between three-dimensional T2 fuzzy sets are thus transferred to two-dimensional range calculations between T1 fuzzy sets. Based on that principle, we also build a T2 fuzzy expected regression model and apply it to the arbitrage pricing theory.

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Section: Introductionmentioning

confidence: 99%