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Axioms
Special Issues
Advances in Fuzzy Logic and Computational Intelligence
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Advances in Fuzzy Logic and Computational Intelligence

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

Deadline for manuscript submissions: 25 October 2024 | Viewed by 2124

Special Issue Editors

Prof. Dr. Eduardo S. Palmeira

E-Mail Website
Guest Editor
Departamento de Ciências Exatas, Universidade Estadual de Santa Cruz, Ilhéus 45662-900, Brazil
Interests: fuzzy logic; fuzzy systems; computational intelligence; artificial intelligence
Prof. Dr. Benjamin Bedregal

E-Mail Website
Guest Editor
Departamento de Informática e Matemática Aplicada, Universidade Federal do Rio Grande do Norte, Natal 59078-900, Brazil
Interests: fuzzy logic; interval mathematics; formal languages; classification and clustering data
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Aluísio Igor R. Fontes

E-Mail Website
Guest Editor
Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte, Pau dos Ferros 59900-000, Brazil
Interests: big data analysis; high-performance computing; digital signal processing; artificial intelligence

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to the most recent advances in fuzzy logic and computational intelligence, from theoretical to applied works. We aim to explore different scopes of fuzzy theory concepts, from classical concepts to the more abstract ones such as intervals and lattices, as well as their relationships with computational intelligence. 

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

  • Fuzzy logic.
  • Fuzzy systems.
  • Interval mathematics.
  • Lattice fuzzy logic.
  • Computational intelligence systems. 

I/We look forward to receiving your contributions.

Prof. Dr. Eduardo S. Palmeira
Prof. Dr. Benjamin Bedregal
Prof. Dr. Aluísio Igor R. Fontes
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. 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 logic
  • fuzzy systems
  • interval mathematics
  • lattice fuzzy logic
  • computational intelligence systems
  • logic
  • interval
  • lattice
  • systems

Published Papers (1 paper)

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Research

16 pages, 513 KiB  
Article
Rough-Fuzzy Based Synthetic Data Generation Exploring Boundary Region of Rough Sets to Handle Class Imbalance Problem
by Mehwish Naushin, Asit Kumar Das, Janmenjoy Nayak and Danilo Pelusi
Axioms 2023, 12(4), 345; https://doi.org/10.3390/axioms12040345 - 31 Mar 2023
Viewed by 1182
Abstract
Class imbalance is a prevalent problem that not only reduces the performance of the machine learning techniques but also causes the lacking of the inherent complex characteristics of data. Though the researchers have proposed various ways to deal with the problem, they have [...] Read more.
Class imbalance is a prevalent problem that not only reduces the performance of the machine learning techniques but also causes the lacking of the inherent complex characteristics of data. Though the researchers have proposed various ways to deal with the problem, they have yet to consider how to select a proper treatment, especially when uncertainty levels are high. Applying rough-fuzzy theory to the imbalanced data learning problem could be a promising research direction that generates the synthetic data and removes the outliers. The proposed work identifies the positive, boundary, and negative regions of the target set using the rough set theory and removes the objects in the negative region as outliers. It also explores the positive and boundary regions of the rough set by applying the fuzzy theory to generate the samples of the minority class and remove the samples of the majority class. Thus the proposed rough-fuzzy approach performs both oversampling and undersampling to handle the imbalanced class problem. The experimental results demonstrate that the novel technique allows qualitative and quantitative data handling. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Computational Intelligence)
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