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CIFD: A Distance for Complex Intuitionistic Fuzzy Set

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Machine Learning for Cyber Security (ML4CS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13655))

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Abstract

Intuitionistic fuzzy set (IFS) has attracted much attention because it can deal with fuzziness and uncertainty more flexibly than traditional fuzzy set. Complex intuitionistic fuzzy set (CIFS) extends intuitionistic fuzzy to the complex plane, which can better express and process uncertain information. In this paper, a novel distance measure complex intuitionistic fuzzy distance (CIFD) is proposed for CIFSs. Firstly, inspired by Tanimoto coefficient, a new similarity measure between CIFSs is proposed. Then, based on the similarity measure, the CIFD is proposed and its non-negativity, non-degeneracy, and symmetry are analyzed. Moreover, when CIFS degenerates into classical IFS, CIFD is also applicable to measure the differences between IFSs. Finally, to illustrate the effectiveness of CIFD, an example is given at the end.

This research is supported by the National Natural Science Foundation of China (No. 62003280), Chongqing Talents: Exceptional Young Talents Project (No. cstc2022ycjh-bgzxm0070), Natural Science Foundation of Chongqing, China (No. CSTB2022NSCQ-MSX0531), and Chongqing Overseas Scholars Innovation Program (No. cx2022024).

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Authors and Affiliations

  1. School of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Yangyang Zhao

  2. School of Big Data and Software Engineering, Chongqing University, Chongqing, 401331, China

    Yangyang Zhao & Fuyuan Xiao

Authors
  1. Yangyang Zhao
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  2. Fuyuan Xiao
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Corresponding author

Correspondence to Fuyuan Xiao .

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Editors and Affiliations

  1. School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, IN, USA

    Yuan Xu

  2. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Hongyang Yan

  3. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Huang Teng

  4. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

  5. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Jin Li

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Zhao, Y., Xiao, F. (2023). CIFD: A Distance for Complex Intuitionistic Fuzzy Set. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13655. Springer, Cham. https://doi.org/10.1007/978-3-031-20096-0_21

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  • DOI: https://doi.org/10.1007/978-3-031-20096-0_21

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-20096-0

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