An Improved Northern Goshawk Optimization Algorithm for Feature Selection-Reference-Cited by-同舟云学术

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An Improved Northern Goshawk Optimization Algorithm for Feature Selection

Published:2024-05-14 Issue:4 Volume:21 Page:2034-2072
ISSN:1672-6529
Container-title:Journal of Bionic Engineering
language:en
Short-container-title:J Bionic Eng

Author:

Xie Rongxiang,Li Shaobo,Wu Fengbin

Funder

the National Natural Science Foundation of China's top-level program

Reserve projects for centralized guidance of local science and technology development funds

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s42235-024-00515-5.pdf

Reference52 articles.

1. Zawbaa, H. M., Emary, E., Grosan, C., & Snasel, V. (2018). Large-dimensionality small-instance set feature selection: A hybrid bio-inspired heuristic approach. Swarm and Evolutionary Computation, 42, 29–42.

2. Manbari, Z., AkhlaghianTab, F., & Salavati, C. (2019). Hybrid fast unsupervised feature selection for high-dimensional data. Expert Systems with Applications, 124, 97–118.

3. Albukhanajer, W. A., Briffa, J. A., & Jin, Y. (2014). Evolutionary multiobjective image feature extraction in the presence of noise. IEEE Transactions on Cybernetics, 45(9), 1757–1768.

4. Xue, B., Zhang, M., Browne, W. N., & Yao, X. (2015). A survey on evolutionary computation approaches to feature selection. IEEE Transactions on evolutionary computation, 20(4), 606–626.

5. Tubishat, M., Ja’afar, S., Alswaitti, M., Mirjalili, S., Idris, N., Ismail, M. A., & Omar, M. S. (2021). Dynamic salp swarm algorithm for feature selection. Expert Systems with Applications, 164, 113873.

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