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New Pythagorean fuzzy-based distance operators and their applications in pattern classification and disease diagnostic analysis

  • S.I.: Interpretation of Deep Learning
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Abstract

Pythagorean fuzzy set is a notion that takes a broader view at intuitionistic fuzzy set with a higher prospect of applications since it has a wider scope compared to IFS. On the other hand, distance measure is an efficient information measure for decision-making via deep learning approach, and thus, this paper proposes a new tri-parametric distance and its weighted version under Pythagorean fuzzy environment with improved performance indexes compared to the hitherto tri-parametric distance measures in the literature. The proposed Pythagorean fuzzy-based distance operators are new approaches because they take into account the three conventional number of parameters of PFSs against the existing practice, and as well incorporate the whole parameters to avoid error due to exclusion as witnessed in other distance operators. Some theorems are presented to validate the new Pythagorean fuzzy distance techniques with regards to its alignment with distance operator’s properties. We demonstrate the applications of the proposed Pythagorean fuzzy distance and its weighted version in cases involving pattern classification and disease diagnosis via deep learning approach where patterns, diseases and patients are presented as Pythagorean fuzzy values. Finally, some comparative analyses of the new tri-parametric Pythagorean fuzzy distance and its weighted version alongside some similar existing distances are presented in terms of the applications to showcase the superiority of the present Pythagorean fuzzy distance techniques.

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Funding

This work is supported by Foundation of Chongqing Municipal Key Laboratory of Institutions of Higher Education ([2017]3), Foundation of Chongqing Development and Reform Commission (2017[1007]), and Foundation of 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 & Johnson Mobolaji Agbetayo

  2. Department of Mathematics, 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

    Shuyu Tang & Xiangguang Dai

Authors
  1. Paul Augustine Ejegwa
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  2. Yuming Feng
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  3. Shuyu Tang
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  4. Johnson Mobolaji Agbetayo
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  5. Xiangguang Dai
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All authors contribute equally to this paper.

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Correspondence to Yuming Feng.

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Ejegwa, P.A., Feng, Y., Tang, S. et al. New Pythagorean fuzzy-based distance operators and their applications in pattern classification and disease diagnostic analysis. Neural Comput & Applic 35, 10083–10095 (2023). https://doi.org/10.1007/s00521-022-07679-3

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  • DOI: https://doi.org/10.1007/s00521-022-07679-3

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