A novel hybrid wrapper–filter approach based on genetic algorithm, particle swarm optimization for feature subset selection

The classification is one of the main technique of machine learning science. In many problems, the data sets have a high dimensionality that the existence of all features is not important to the purpose of the problem, and this will decrease the accuracy and performance of the algorithm. In this sit...

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Veröffentlicht in:	Journal of ambient intelligence and humanized computing Jg. 11; H. 3; S. 1105 - 1127
Hauptverfasser:	Moslehi, Fateme, Haeri, Abdorrahman
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2020 Springer Nature B.V
Schlagworte:	Accuracy Artificial Intelligence Artificial neural networks Classification Computational Intelligence Data mining Datasets Efficiency Energy consumption Engineering Evolutionary algorithms Exploitation Feature selection Genetic algorithms Heuristic Internet Machine learning Neural networks Optimization Original Research Particle swarm optimization Radio frequency identification Robotics and Automation Sensors Traveling salesman problem User Interfaces and Human Computer Interaction Hybrid (wrapper–filter) approach Feature selection Multi-objective optimization Genetic algorithm Particle swarm optimization (PSO)
ISSN:	1868-5137, 1868-5145
Online-Zugang:	Volltext
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