This journal is devoted to the theory, design and applications of fuzzy systems, ranging from hardware to software. Emphasis will be given to engineering applications.

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Call for Nominations / Applications for the position of Editor-in-Chief of the IEEE Transactions on Fuzzy Systems

The IEEE Transactions on Fuzzy Systems (TFS) is a publication of the IEEE Computational Intelligence
Society (CIS). TFS considers high-quality papers that deal with the theory, design or applications of
fuzzy systems ranging from hardware to software.
The IEEE CIS Executive Committee has formed an Adhoc Search Committee to invite
nominations/applications for the position of Editor-in-Chief for TFS. The Editor-in-Chief appointment
is for a 2-year term starting 1 January 2023. Nominees/applicants should be dedicated volunteers with
outstanding research profiles and extensive editorial experience. The nomination/application package
should include complete CV along with a separate description (max 300 words/topic) on each of the
following items:
  • Vision Statement;
  • Editorial Experience;
  • Summary of publishing experience in IEEE journals/magazines;
  • IEEE Volunteer Experience;
  • Institutional Support;
  • Current service and administrative commitments;
  • Networking with the Community;
  • Challenges, if any, faced by the publication, and how to deal with them;
  • Why the candidate considers himself/herself fit for this position?
The nomination/application package should be sent as a single PDF file through email to both Prof.
Kay Chen Tan ( and Jo-Ellen Snyder ( by May 20, 2022.

Featured Paper

A Metahierarchical Rule Decision System to Design Robust Fuzzy Classifiers Based on Data Complexity
Authors: Javier Cózar, Alberto Fernández, Francisco Herrera, José A. Gámez
Publication: IEEE Transactions on Fuzzy Systems (TFS)
Issue: Volume 27, Issue 4 – April 2019
Pages: 701-715

Abstract: There is a wide variety of studies that propose different classifiers to solve a large amount of problems in distinct classification scenarios. The no free lunch theorem states that if we use a big enough set of varied problems, all classifiers would be equivalent in performance. From another point of view, the performance of the classifiers is dependant of the scope and properties of the datasets. In this sense, new proposals on the topic often focus on a given context, aiming at improving the related state-of-the-art approaches. Data complexity metrics have been traditionally used to determine the inner characteristics of datasets. This way, researchers are able to categorize the problems in different scenarios. Then, this taxonomy can be applied to determine inner characteristics of the datasets in order to determine intervals of good and bad behavior for a given classifier. In this paper, we will take advantage of the data complexity metrics in order to design a fuzzy metaclassifier. The final goal is to create decision rules based on the inner characteristics of the data to apply a different version of the fuzzy classifier for a given problem. To do so, we will make use of the FARC-HD classifier, an evolutionary fuzzy system that has led to different extensions in the specialized literature. Experimental results show the goodness of this novel approach as it is able to outperform all versions of FARC-HD on a wide set of problems, and obtain competitive results (in terms of performance and interpretability) versus two selected state-of-the-art rule-based classification system, C4.5 and FURIA.

Index Terms: Data complexity metrics (DCM), evolutionary fuzzy system, fuzzy rule based classification system, metaclassifier
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