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Mixed fuzzy least absolute regression analysis with quantitative and probabilistic linguistic information

2019; Elsevier BV; Volume: 387; Linguagem: Inglês

10.1016/j.fss.2019.03.004

1872-6801

Lisheng Jiang, Huchang Liao,

Fuzzy Logic and Control Systems

Regression analysis is widely used in evaluation and prediction, and fuzzy least absolute regression is preferred when data is fat-tailed or out-linear. Given that the probabilistic linguistic term set is a powerful tool in expressing evaluators' complex linguistic perceptions, this paper incorporates the probabilistic linguistic term set to the fuzzy least absolute regression and builds a fuzzy regression model with mixed types of inputs. To achieve this goal, this paper introduces the concept of double-cut set of the probabilistic linguistic term set. Then, new operations of probabilistic linguistic term sets based on the double-cut sets are investigated. A mixed fuzzy least absolute regression model is proposed and a linear programming is introduced to work out the fuzzy regression parameters. A numerical example concerning the house lease price evaluation under the shared economy is provided to validate the applicability of the proposed model. The study ends with some concluding remarks.