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Structured Representation of Fuzzy Data by Bipolar Fuzzy Hypergraphs

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Machine Learning for Cyber Security (ML4CS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13657))

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Abstract

The main aim of the graph model is to implement the structured representation of data, and the hypergraph can be regarded as a promotion of graph which is used in a wider range of data representation scenarios. Bipolar fuzzy sets are employed to describe the uncertainty of the positive and negative of objectives, and fuzzy graphs are modelled to structure the description of uncertain data. In this work, for bipolar Pythagorean fuzzy set (BPFS) and bipolar intuitionistic fuzzy set (BIFS), the corresponding definitions of bipolar Pythagorean fuzzy hypergraph (BPFH) and bipolar intuitionistic fuzzy hypergraph (BIFH) are given, and they are described from the perspective of set theory. The characteristics under the bipolar hypergraph framework of this representation are discussed.

Supported by Natural Science Foundation of Jiangsu Province, China (BK20191032) and University Philosophy and Social Science research projects of Jiangsu Province (2020SJA1195).

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Author information

Authors and Affiliations

  1. School of Chemical and Environmental Engineering, Jiangsu University of Technology, Changzhou, 213001, China

    Juanjuan Lu

  2. School of Computer Engineering, Jiangsu University of Technology, Changzhou, 213001, China

    Linli Zhu

  3. School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China

    Wei Gao

Authors
  1. Juanjuan Lu
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  2. Linli Zhu
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  3. Wei Gao
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Corresponding author

Correspondence to Juanjuan Lu .

Editor information

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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Lu, J., Zhu, L., Gao, W. (2023). Structured Representation of Fuzzy Data by Bipolar Fuzzy Hypergraphs. 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 13657. Springer, Cham. https://doi.org/10.1007/978-3-031-20102-8_52

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  • DOI: https://doi.org/10.1007/978-3-031-20102-8_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20101-1

  • Online ISBN: 978-3-031-20102-8

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