Design of Reinforced Fuzzy Model Driven to Feature Selection Through Univariable-Based Correlation and Multivariable-Based Determination Coefficient Analysis-Reference-Cited by-同舟云学术

Design of Reinforced Fuzzy Model Driven to Feature Selection Through Univariable-Based Correlation and Multivariable-Based Determination Coefficient Analysis

Published:2022-10 Issue:10 Volume:30 Page:4224-4238
ISSN:1063-6706
Container-title:IEEE Transactions on Fuzzy Systems
language:
Short-container-title:IEEE Trans. Fuzzy Syst.

Author:

Kim Eun-Hu¹^ORCID,Oh Sung-Kwun²^ORCID,Pedrycz Witold³^ORCID,Fu Zunwei⁴^ORCID

Affiliation:

1. Research Center for Big Data and Artificial Intelligence, Linyi University, Linyi, China

2. School of Electrical and Electronic Engineering, University of Suwon, Hwaseong-si, South Korea

3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

4. ICT School, University of Suwon, Hwaseong-si, South Korea

Funder

National Research Foundation of Korea

Ministry of Science and ICT, South Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/91/9911789/09697361.pdf?arnumber=9697361

Reference42 articles.

1. STAC: A web platform for the comparison of algorithms using statistical tests

2. METSK-HDe: A multiobjective evolutionary algorithm to learn accurate TSK-fuzzy systems in high-dimensional and large-scale regression problems

3. Reinforced hybrid interval fuzzy neural networks architecture: Design and analysis

4. Reinforced Fuzzy Clustering-Based Ensemble Neural Networks

5. Relief-based feature selection: Introduction and review

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