Empirical analyses of genetic algorithm and grey wolf optimiser to improve their efficiency with a new multi-objective weighted fitness function for feature selection in machine learning classification: the roadmap-Reference-Cited by-同舟云学术

Empirical analyses of genetic algorithm and grey wolf optimiser to improve their efficiency with a new multi-objective weighted fitness function for feature selection in machine learning classification: the roadmap

Published:2022-02-13 Issue:2 Volume:35 Page:171-206
ISSN:0952-813X
Container-title:Journal of Experimental & Theoretical Artificial Intelligence
language:en
Short-container-title:Journal of Experimental & Theoretical Artificial Intelligence

Author:

Davahli Azam¹,Shamsi Mahboubeh²,Abaei Golnoush³,Khosravi Arash⁴

Affiliation:

1. Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran

2. Faculty of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran

3. Department of Software Engineering, School of Information Technology, Monash University Malaysia, Malaysia

4. Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Theoretical Computer Science,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/0952813X.2021.1960627

Reference86 articles.

1. Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components

2. Breast cancer diagnosis using GA feature selection and Rotation Forest

3. Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection

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