Scope

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.

Impact Score

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Journal Citation Metrics Journal Citation Metrics such as Impact Factor, Eigenfactor Score™ and Article Influence Score™ are available where applicable. Each year, Journal Citation Reports© (JCR) from Thomson Reuters examines the influence and impact of scholarly research journals. JCR reveals the relationship between citing and cited journals, offering a systematic, objective means to evaluate the world's leading journals. Find out more about IEEE Journal Rankings.

Special Note:

In order to support world-wide efforts in fighting COVID-19, the IEEE Computational Intelligence Society (IEEE CIS) has set up a program - the COVID-19 Initiative. Under this initiative, IEEE TFS will expedite, as far as possible, the processing of all articles submitted with primary focus on COVID-19:

  • We have set-up a special Fast-Track to process COVID-19 focused manuscripts. All papers submitted to this Fast Track will undergo a fast review process, with the targeted first decision within 4 weeks. If the paper reaches the revision stage, the author(s) will then have 2 weeks to revise, followed by another round of review within 3 weeks to reach a final decision. We will aim to reach a final decision for these manuscripts within 9 weeks.
  • If you decide to submit to the COVID-19 Initiative, please make sure you select the ‘Special Issue’ option and upload a cover letter stating that the paper is for the ‘COVID-19 Initiative’.
  • Your manuscript must be within the scope of IEEE TFS, as well as having a research focus on COVID-19.
  • If accepted TFS will arrange to publish and print these articles as quickly as possible. Furthermore, all such articles will be published, free-of-charge to authors and readers, and free access for one year from the date of the publication to enable the research findings to be disseminated widely and freely to other researchers and the community at large.

We look forward to your submissions to TFS.

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
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8444672

 

Lotfi A. Zadeh In Memoriam

Zadeh

Recounts the career and achievements of Lotfi A. Zadeh, who passed away September 6, 2017.

P. P. Bonissone and E. H. Ruspini, "Lotfi A. Zadeh In Memoriam," in IEEE Transactions on Fuzzy Systems, vol. 25, no. 5, pp. 1021-1022, Oct. 2017.

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8057934&isnumber=8057897