Adaptive feature selection using v-shaped binary particle swarm optimization

Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate th...

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Veröffentlicht in:	PloS one Jg. 12; H. 3; S. e0173907
Hauptverfasser:	Teng, Xuyang, Dong, Hongbin, Zhou, Xiurong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 30.03.2017 Public Library of Science (PLoS)
Schlagworte:	Adaptive algorithms Algorithms Analysis Artificial intelligence Classification Computer and Information Sciences Computer science Correlation analysis Cybernetics Data mining Data Mining - statistics & numerical data Data processing Datasets Drafting Engineering Entropy Entropy (Information theory) Evaluation Evolution Expert systems Game theory Gene mapping Gravity Humans Image processing Information processing Information theory Instrumentation Learning algorithms Machine learning Machine Learning - statistics & numerical data Mathematical models Methods Neurocomputing Noise Optimization Optimization theory Particle swarm optimization Pattern recognition Physical Sciences Physical training Police R&D Redundancy Research & development Research and Analysis Methods Searching Social interactions Social organization Statistical analysis Support Vector Machine - statistics & numerical data Swarm intelligence Technology Transfer functions China
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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