Improved firefly algorithm for feature selection with the ReliefF-based initialization and the weighted voting mechanism-Reference-Cited by-同舟云学术

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Improved firefly algorithm for feature selection with the ReliefF-based initialization and the weighted voting mechanism

Published:2022-09-09 Issue:1 Volume:35 Page:275-301
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Yong Xin,Gao Yue-lin

Funder

Key Project of Ningxia Natural Science Foundation

Major scientific Research Project of Northern University for Nationalities

Natural Science Foundation of NingXia Hui Autonomous Region

Research Startup Foundation of North Minzu University

National Natural Science Foundation of China

First-class Discipline Construction Fund project of Ningxia Higher Education

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-022-07755-8.pdf

Reference52 articles.

1. Alican D, Derya B (2021) Machine learning and data mining in manufacturing. Expert Syst Appl 166:114060. https://doi.org/10.1016/j.eswa.2020.114060

2. Jie C, Jiawei L, Shulin W, Sheng Y (2018) Feature selection in machine learning: a new perspective. Neurocomputing 300:70–79. https://doi.org/10.1016/j.neucom.2017.11.077

3. Mohammad T, Majdi MM, Ali AH, Hossam F, Ibrahim A, Seyedali M, Hamido F (2019) An evolutionary gravitational search-based feature selection. Inf Sci 497:219–239. https://doi.org/10.1016/j.ins.2019.05.038

4. Lei Y, Huan L (2014) Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res 5, 1205–1224 (2004). Springer Nature 2021 LATEX template Article Title 19 https://doi.org/10.1023/B:JODS.0000045365.56394.b4

5. Mehrdad R, Kamal B, Elahe N, Saman F (2021) Review of swarm intelligence-based feature selection methods. Eng Appl Artif Intell 100:104210. https://doi.org/10.1016/j.engappai.2021.104210

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