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A New Method for Assigning Hesitation Based on an IFS Distance Metric

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Artificial Intelligence (CICAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13606))

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Abstract

Firstly, we analyze the shortcomings of the existing intuitionistic fuzzy set distance measure, and then propose a method for assigning the hesitation degree of intuitionistic fuzzy sets by considering the fuzzy meanings expressed by the membership degree, non-membership degree, and hesitation degree, and prove through theoretical derivation that the intuitionistic fuzzy set distance measure under the new method satisfies the conditions of the distance measure, and illustrate the rationality of the method with the analysis and comparison of numerical examples. Finally, the method is applied to practical medical diagnosis. The experimental results show that the new method has certain validity and feasibility.

This work is supported by the National Natural Science Foundation of China under Grant 62066001 and the Natural Science Foundation of Ningxia Province under Grant 2022AAC03238.

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Authors and Affiliations

  1. School of Mathematics and Information Science, North Minzu University, Yinchuan, 750021, China

    Changlin Xu & Yaqing Wen

Authors
  1. Changlin Xu
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  2. Yaqing Wen
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Correspondence to Changlin Xu .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Xiaomi Inc., Beijing, China

    Daniel Povey

  3. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  4. JD Explore Academy, Beijing, China

    Tao Mei

  5. Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

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Xu, C., Wen, Y. (2022). A New Method for Assigning Hesitation Based on an IFS Distance Metric. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13606. Springer, Cham. https://doi.org/10.1007/978-3-031-20503-3_1

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  • DOI: https://doi.org/10.1007/978-3-031-20503-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20502-6

  • Online ISBN: 978-3-031-20503-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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