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Pattern Recognition Based on an Improved Szmidt and Kacprzyk’s Correlation Coefficient in Pythagorean Fuzzy Environment

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Advances in Neural Networks – ISNN 2020 (ISNN 2020)

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Abstract

Correlation measure is an applicable tool in Pythagorean fuzzy domain for resolving problems of multi-criteria decision-making (MCDM). Szmidt and Kacprzyk proposed a correlation coefficient in intuitionistic fuzzy domain (IFSs) by considering the orthodox parameters of IFSs. Nonetheless, the approach contradicts the axiomatic description of correlation coefficient between IFSs in literature. In this paper we modify the Szmidt and Kacprzyk’s approach for measuring correlation coefficient between IFSs to satisfy the axiomatic description of correlation coefficient, and extend the modified version to Pythagorean fuzzy environment. Some numerical illustrations are considered to ascertain the merit of the modified version over Szmidt and Kacprzyk’s approach. Finally, the proposed correlation coefficient measure is applied to resolve some pattern recognition problems. In recap, the goal of this paper is to modify Szmidt and Kacprzyk’s correlation coefficient for IFSs, extend it to Pythagorean fuzzy context with pattern recognition applications.

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Acknowledgements

This work is supported by the Foundations of Chongqing Municipal Key Laboratory of Institutions of Higher Education ([2017]3), Chongqing Development and Reform Commission (2017[1007]), and Chongqing Three Gorges University.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Information Processing and Control, Chongqing Three Gorges University, Wanzhou, Chongqing, 404100, China

    Paul Augustine Ejegwa & Yuming Feng

  2. Department of Mathematics/Statistics/Computer Science, University of Agriculture, P.M.B. 2373, Makurdi, Nigeria

    Paul Augustine Ejegwa

  3. Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology, Chongqing Three Gorges University, Wanzhou, Chongqing, 404100, China

    Wei Zhang

Authors
  1. Paul Augustine Ejegwa
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  2. Yuming Feng
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  3. Wei Zhang
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Correspondence to Yuming Feng .

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Editors and Affiliations

  1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China

    Min Han

  2. School of Science, Harbin Institute of Technology, Weihai, China

    Sitian Qin

  3. School of Engineering and Applied Sciences, University of the District of Columbia, Washington, DC, USA

    Nian Zhang

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Ejegwa, P.A., Feng, Y., Zhang, W. (2020). Pattern Recognition Based on an Improved Szmidt and Kacprzyk’s Correlation Coefficient in Pythagorean Fuzzy Environment. In: Han, M., Qin, S., Zhang, N. (eds) Advances in Neural Networks – ISNN 2020. ISNN 2020. Lecture Notes in Computer Science(), vol 12557. Springer, Cham. https://doi.org/10.1007/978-3-030-64221-1_17

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  • DOI: https://doi.org/10.1007/978-3-030-64221-1_17

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