&lt;title&gt;Detecting outliers in fuzzy regression analysis with asymmetric trapezoidal fuzzy data&lt;/title&gt;

Maleki, A.; Pasha, Einollah; Yari, Gh.; Razzaghnia, Tahereh

doi:10.1117/12.923177

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Outlier Detection In Symmetric Triangular Fuzzy Numbers1

Introduction1

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2019

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Cited by 1 publication

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“…Maleki et al [24] proposed a new method in trapezoidal fuzzy data when the outlier is detected. They defined a new parameters called "H" and replaced it to the "h" in main fuzzy regression model.…”

Section: Outlier Detection In Symmetric Triangular Fuzzy Numbersmentioning

confidence: 99%

See 1 more Smart Citation

Regression parameters prediction in data set with outliers using neural network

Razzaghnia¹

2019

Hacettepe Journal of Mathematics and Statistics

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Popular regression techniques often suffer at the presence of data outliers. The different methods have proposed to make smaller the effect of the outlier on the parameter estimates. In this study, an algorithm has been addressed based on Adaptive network based fuzzy inference system to define the unknown parameters of regression model where dependent variable has outlier. So, three numerical examples are solved to test the activity of the proposed algorithm in regression model estimation. Also, the obtained results from the different methods, such as linear programming (LP) and fuzzy weights with linear programming (FWLP) are compared together. The results show that the proposed method is not to be affected the outliers in the solving process.

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Section: Outlier Detection In Symmetric Triangular Fuzzy Numbersmentioning

confidence: 99%

“…Different methods have been proposed for reducing the influence of outliers (see, e.g. [2,16,24]).…”

Section: Introductionmentioning

confidence: 99%