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Outlier Detection in Fuzzy Regressions

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Recent Developments and the New Direction in Soft-Computing Foundations and Applications

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 393))

Abstract

Existence of outliers among the observation data leads to inaccurate results in modeling. Either elimination or reduction of the outlier data influence is two ways to prevent their negative effect on the modeling. The approach of impact reduction is taken into account in dealing with the mentioned problem in fuzzy regression, where the input is crisp and the output data is fuzzy. The main idea is based on optimizing a weighted target function into fuzzy regression. Some experiments and simulation studies are designed to show its performance in the presence of different kinds of outliers in the data set. The experimental results suggest that the proposed model is capable of dealing with the data set contaminated by outliers and has high prediction accuracy. The proposed fuzzy regression method is capable of determining the weigh of the outlying data points.

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Author information

Authors and Affiliations

  1. Faculty of Mathematical Sciences and Computer, Shahid Chamran University of Ahvaz, 61355-83151, Ahvaz, Iran

    Jalal Chachi

  2. School of Engineering Science, College of Engineering, University of Tehran, 1417613131, Tehran, Iran

    S. Mahmoud Taheri

Authors
  1. Jalal Chachi
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  2. S. Mahmoud Taheri
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Corresponding author

Correspondence to S. Mahmoud Taheri .

Editor information

Editors and Affiliations

  1. Department of Information Technology and Programming, Azerbaijan Technical University, Baku, Azerbaijan

    Shahnaz N. Shahbazova

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

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Chachi, J., Taheri, S.M. (2021). Outlier Detection in Fuzzy Regressions. In: Shahbazova, S.N., Kacprzyk, J., Balas, V.E., Kreinovich, V. (eds) Recent Developments and the New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 393. Springer, Cham. https://doi.org/10.1007/978-3-030-47124-8_24

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