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

Authors:
H. Shakouri G.;R. Nadimi
Affiliations:
School of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran;School of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran
Venue:
Applied Soft Computing
Year:
2013

Citing 10
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Abstract

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.