GOG-MBSHO: multi-strategy fusion binary sea-horse optimizer with Gaussian transfer function for feature selection of cancer gene expression data

Cancer gene expression data has the characteristics of high-dimensional, multi-text and multi-classification. The problem of cancer subtype diagnosis can be solved by selecting the most representative and predictive genes from a large number of gene expression data. Feature selection technology can...

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Veröffentlicht in:	The Artificial intelligence review Jg. 57; H. 12; S. 347
Hauptverfasser:	Wang, Yu-Cai, Song, Hao-Ming, Wang, Jie-Sheng, Song, Yu-Wei, Qi, Yu-Liang, Ma, Xin-Ru
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.12.2024 Springer Springer Nature B.V
Schlagworte:	Accuracy Algorithms Artificial Intelligence Cancer Classification Computer Science Convergence Data Datasets Exploitation Feature selection Function Gene expression Genes Genetic aspects Global local relationship Horses Intelligence Medical diagnosis Movement Optimization Simulation Strategies Technology assessment Transfer functions Trigonometric functions Feature selection Gaussian transfer function Cancer gene expression Multi-strategy fusion Sea-horse optimizer
ISSN:	1573-7462, 0269-2821, 1573-7462
Online-Zugang:	Volltext
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