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Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

Author

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  • Abbas Mardani
  • Mehrbakhsh Nilashi
  • Jurgita Antucheviciene
  • Madjid Tavana
  • Romualdas Bausys
  • Othman Ibrahim

Abstract

Rough set theory has been used extensively in fields of complexity, cognitive sciences, and artificial intelligence, especially in numerous fields such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning. Rough sets models, which have been recently proposed, are developed applying the different fuzzy generalisations. Currently, there is not a systematic literature review and classification of these new generalisations about rough set models. Therefore, in this review study, the attempt is made to provide a comprehensive systematic review of methodologies and applications of recent generalisations discussed in the area of fuzzy-rough set theory. On this subject, the Web of Science database has been chosen to select the relevant papers. Accordingly, the systematic and meta-analysis approach, which is called “PRISMA,” has been proposed and the selected articles were classified based on the author and year of publication, author nationalities, application field, type of study, study category, study contribution, and journal in which the articles have appeared. Based on the results of this review, we found that there are many challenging issues related to the different application area of fuzzy-rough set theory which can motivate future research studies.

Suggested Citation

  • Abbas Mardani & Mehrbakhsh Nilashi & Jurgita Antucheviciene & Madjid Tavana & Romualdas Bausys & Othman Ibrahim, 2017. "Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature," Complexity, Hindawi, vol. 2017, pages 1-33, October.
    • Handle: RePEc:hin:complx:1608147
    DOI: 10.1155/2017/1608147
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    References listed on IDEAS

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    3. Ravil I. Mukhamediev & Yelena Popova & Yan Kuchin & Elena Zaitseva & Almas Kalimoldayev & Adilkhan Symagulov & Vitaly Levashenko & Farida Abdoldina & Viktors Gopejenko & Kirill Yakunin & Elena Muhamed, 2022. "Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges," Mathematics, MDPI, vol. 10(15), pages 1-25, July.

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