Abstract
In this paper, we propose fuzzy regression analysis based on a quadratic programming approach. In fuzzy regression analysis, a quadratic programming approach gives more diverse spread coefficients than a linear programming approach. Moreover, a quadratic programming approach can integrate the central tendency of least squares and the possibilistic properties of fuzzy regression. Due to the characteristic of the quadratic programming problem, the proposed approach can obtain the optimal regression model representing possibilistic properties with the central tendency. In this approach, we classify the given data into two groups, i.e., the center-located group and the remaining group. Then, the upper and the lower approximation models can be obtained based on the classification result. By changing the weight coefficients of the objective function in the quadratic programming problem, we can analyze the given data in various angles.
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