Outlier detection in fuzzy linear regression with crisp input–output by linguistic variable view

Existence of outlier data among the observation data leads to inaccurate results in modeling. Detection to omit or lessen the impact of such data has a significant effect to make corrections in a model. Either elimination or reduction of the outlier data influence is two ways to prevent their negative effect on the modeling. Both approaches of elimination and impact reduction are taken into account in dealing with the mentioned problem in fuzzy regression, where both the input and output data are non-fuzzy. The main idea is considered based on linguistic variables and possibility concept as well as ordinary regression to deal with the outlier data. Several examples as well as a case study are put into effect to show the capability of proposed approach.

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► The problem of outlier data affects results of regression analyses and leads to inaccurate estimates and forecasts.
► It is undertaken here using linguistic variables and possibility concept along with regression to reduce the impact of the outlier data.
► The areas in fuzzy numbers generated for each sample data is considered as a measure for the uncertainty to detect outliers.
► The both h-level and spread of the fuzzy numbers contribute to recognize the outlier data intelligently and lessen their effects.
► The results of this new approach applied to several examples show high performance of the proposed method.