A Type 2 Fuzzy C-Regression Method

Asli Celikyilmaz and I. Burhan Turksen.

This paper presents a type-2 genetic fuzzy inference system based on fuzzy c-regression method clustering algorithm, to identify uncertainties in hyperplane shaped fuzzy clusters. The uncertainty in learning parameters of the new system is identified by type-2 fuzzy sets. Genetic algorithm is used to optimize the secondary membership grades of the type-2 fuzzy sets. Transductive reasoning, instead of inductive reasoning, is used to develop a local model for every new vector, based on some closest vectors from the given database. This study is novel because it presents a new methodology to identify type-2 fuzzy sets. The results of comparative experiments on financial forecasting problem domain are encouraging.

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