An interval-based approach to fuzzy regression for fuzzy input-output data

A novel approach is introduced to construct a fuzzy regression model when the data available of independent and dependent variables are fuzzy numbers. The approach, consisting on the least-squares method, uses the α-level sets of fuzzy observations to estimate the crisp parameters of the model. A competitive study shows the performance and efficiency of the proposed approach with respect to some well-known methods.