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Axioms
Special Issues
Advances in Fuzzy Theory and Decision-Making Theory
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Advances in Fuzzy Theory and Decision-Making Theory

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: closed (25 October 2024) | Viewed by 1497

Special Issue Editor

Dr. Honggang Peng

E-Mail Website
Guest Editor
Department of Information Management, Chongqing University, Chongqing, China
Interests: decision-making theory and application; intelligent decision-making; data analysis and forecasting

Special Issue Information

Dear Colleagues,

Decision-making exists in a variety of sectors (such as politics, economics, industry, medicine, finance, management, etc.) and in people's daily lives. In recent years, many scholars have been drawn to decision-making theory and methodology, resulting in the development of a series of decision-making paradigms and promising research results. In particular, with the rapid growth and broad application of high technology, such as the Internet, big data, and artificial intelligence, the decision-making elements and environment have been altered, necessitating the study of new decision-making theories and methods.

This Special Issue is dedicated to disseminating the latest advancements in the field of decision-making and furthering the development of decision-making theory and methods. In this Special Issue, original research articles and reviews are welcome. Research areas may include, but are not limited to, the following:

  • Fuzzy decision-making (fuzzy theory);
  • Multi-attribute decision-making (aggregation, measurement, and outranking);
  • Group decision-making (large-scale groups and social networks);
  • Consensus decision-making (cooperative and non-cooperative behaviors, dynamic consensus);
  • Decision-making based on preference relations (preference theory);
  • Intelligent decision-making (intelligent models and algorithms);
  • Data-driven decision-making (data processing and analysis, data description, and practical method application).

I look forward to receiving your contributions. 

Dr. Honggang Peng
Guest Editor

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. Axioms is an international peer-reviewed open access monthly 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 2400 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 decision-making
  • multi-attribute decision-making
  • group decision-making
  • consensus decision-making
  • decision-making based on preference relations
  • intelligent decision-making
  • data-driven decision-making

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Published Papers (1 paper)

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Research

23 pages, 1345 KiB  
Article
Fuzzy Decision Tree Based on Fuzzy Rough Sets and Z-Number Rules
by Boya Zhu, Jingqian Wang and Xiaohong Zhang
Axioms 2024, 13(12), 836; https://doi.org/10.3390/axioms13120836 - 28 Nov 2024
Cited by 1 | Viewed by 861
Abstract
The decision tree algorithm is widely used in various classification problems due to its ease of implementation and strong interpretability. However, information in the real world often has uncertainty and partial reliability, which poses challenges for classification tasks. To address this issue, this [...] Read more.
The decision tree algorithm is widely used in various classification problems due to its ease of implementation and strong interpretability. However, information in the real world often has uncertainty and partial reliability, which poses challenges for classification tasks. To address this issue, this paper proposes a fuzzy decision tree based on fuzzy rough sets and Z-numbers, aimed at enhancing the decision tree’s ability to handle fuzzy and uncertain information. In the aspect of rule extraction, we combine the fuzzy rough set model to propose a fuzzy confidence based on lower approximation as a metric for attribute selection, effectively addressing the role of imprecise knowledge in classification. In terms of the tree structure, the concept of Z-numbers is introduced, specifically focusing on the fuzzy constraint reliability B, making the information representation more aligned with human evaluation habits, as well as using Z-number rules to replace traditional fuzzy rules in constructing the fuzzy decision tree. Furthermore, as generating Z-numbers still presents certain challenges, this paper also establishes a method for reasonably generating Z-numbers in situations with limited information, utilizing the generated fuzzy constraint reliability B to adjust fuzzy numbers A. Finally, the proposed decision tree algorithm is experimentally compared with other classifiers, and the results indicate that this algorithm demonstrates higher classification accuracy and a more concise tree structure when handling datasets containing fuzzy and uncertain factors. This research enriches the existing research on fuzzy decision trees and shows greater potential in solving practical problems. Full article
(This article belongs to the Special Issue Advances in Fuzzy Theory and Decision-Making Theory)
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