Innovative Feature Selection Method Based on Hybrid Sine Cosine and Dipper Throated Optimization Algorithms-Reference-Cited by-同舟云学术

Innovative Feature Selection Method Based on Hybrid Sine Cosine and Dipper Throated Optimization Algorithms

Published:2023 Issue: Volume:11 Page:79750-79776
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Abdelhamid Abdelaziz A.¹^ORCID,El-Kenawy El-Sayed M.²^ORCID,Ibrahim Abdelhameed³^ORCID,Eid Marwa Metwally⁴,Khafaga Doaa Sami⁵^ORCID,Alhussan Amel Ali⁵^ORCID,Mirjalili Seyedali⁶,Khodadadi Nima⁷,Lim Wei Hong⁸,Shams Mahmoud Y.⁹

Affiliation:

1. Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

2. Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, Egypt

3. Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

4. Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt

5. Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

6. Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, QLD, Australia

7. Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA

8. Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, Malaysia

9. Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt

Funder

Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia, through Researchers Supporting

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10194924.pdf?arnumber=10194924

Reference63 articles.

1. Feature Selection Using Metaheuristic Algorithms: Concept, Applications and Population Based Comparison

2. Novel Meta-Heuristic Algorithm for Feature Selection, Unconstrained Functions and Engineering Problems

3. Imbalanced Dataset Optimization with New Resampling Techniques

4. Sine cosine optimization algorithm for feature selection

5. A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy

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