Advances in Fuzzy Rough Sets and Intelligent Computing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 382

Special Issue Editors


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Guest Editor
College of Artificial Intelligence, Southwest University, Chongqing 400715, China
Interests: granular computing; rough sets; feature selection
School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
Interests: granular computing; three-way decision; group decision
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Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to a special issue of Mathematics focused on Fuzzy Rough Sets and Intelligence Computing. This special issue aims to explore the intersection of fuzzy rough set theory and intelligent computing techniques. Fuzzy rough sets provide a powerful mathematical framework for handling uncertainty, imprecision, and vagueness in data analysis, while intelligent computing incorporates methods such as machine learning, artificial intelligence, and data mining for decision support and problem-solving. By combining these two areas, we can address complex real-world problems that involve uncertain or incomplete information, with applications spanning across multiple fields such as artificial intelligence, machine learning, data mining, decision-making, and pattern recognition.

This Special Issue aims to the latest developments in fuzzy rough set theory, intelligent computing, and their integration in tackling challenging problems. We invite both theoretical and applied contributions, including novel algorithms, methods, and case studies that explore how fuzzy rough sets can enhance intelligent computing processes for improved decision-making and problem-solving.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Fuzzy Rough Set Theory
  • Intelligent Computing Techniques
  • Data Mining and Knowledge Discovery
  • Multi-Criteria Decision Making
  • Hybrid Models
  • Applications in Healthcare
  • Uncertainty Modeling
  • Big Data and Cloud Computing

I look forward to receiving your contributions.

Dr. Wentao Li
Dr. Chao Zhang
Dr. Tao Zhan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fuzzy rough sets
  • intelligent computing
  • machine learning
  • decision support systems
  • multi-criteria decision making

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Published Papers (1 paper)

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Research

24 pages, 355 KiB  
Article
A Generalized Multigranulation Rough Set Model by Synthesizing Optimistic and Pessimistic Attitude Preferences
by Hongwei Wang, Huilai Zhi, Yinan Li, Daxin Zhu and Jianbing Xiahou
Mathematics 2025, 13(9), 1367; https://doi.org/10.3390/math13091367 - 22 Apr 2025
Viewed by 109
Abstract
Attitude preference plays an important role in multigranulation data mining and decision-making. That is, different attitude preferences lead to different results. At present, both optimistic and pessimistic multigranulation rough sets have been studied independently and thoroughly. But, sometimes, a decision-maker’s attitude may vary, [...] Read more.
Attitude preference plays an important role in multigranulation data mining and decision-making. That is, different attitude preferences lead to different results. At present, both optimistic and pessimistic multigranulation rough sets have been studied independently and thoroughly. But, sometimes, a decision-maker’s attitude may vary, which may shift either from an optimistic to pessimistic view of decision-making or from a pessimistic to optimistic view of decision-making. In this paper, we propose a novel multigranulation rough set model, which synthesizes optimistic and pessimistic attitude preferences. Specifically, we put forward methods to evaluate the attitude preferences in four types of decision systems. Two main issues are addressed with regard to attitude preference dependency. The first is concerned with the common attitude preference, while the other relates to the sequence-dependent attitude preference. Finally, we present three types of multigranulation rough set models from the perspective of the different connection methods between optimistic and pessimistic attitude preferences. Full article
(This article belongs to the Special Issue Advances in Fuzzy Rough Sets and Intelligent Computing)
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