A novel hybrid BPSO–SCA approach for feature selection

Nature is a great source of inspiration for solving complex problems in real-world. In this paper, a hybrid nature-inspired algorithm is proposed for feature selection problem. Traditionally, the real-world datasets contain all kinds of features informative as well as non-informative. These features...

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Veröffentlicht in:	Natural computing Jg. 20; H. 1; S. 39 - 61
Hauptverfasser:	Kumar, Lalit, Bharti, Kusum Kumari
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.03.2021 Springer Nature B.V
Schlagworte:	Artificial Intelligence Cluster analysis Clustering Complex Systems Complexity Computer Science Datasets Evolutionary Biology Feature selection Gene expression Machine learning Optimization algorithms Particle swarm optimization Processor Architectures Search algorithms Theory of Computation Transfer functions Trigonometric functions Clustering indices Binary artificial bee colony algorithm Binary dragonfly algorithm Feature selection Binary particle swarm optimization Sine cosine algorithm Binary moth flame optimization Binary whale optimization algorithm
ISSN:	1567-7818, 1572-9796
Online-Zugang:	Volltext
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