An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm

Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a r...

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Detalhes bibliográficos

Principais autores:	Ziyi Chen, Wenbai Liu
Formato:	Artigo
Idioma:	Inglês
Publicado em:	IEEE 2020-01-01
coleção:	IEEE Access
Assuntos:	Machine learning forecasting problem K-means clustering method chaotic slime mould algorithm support vector regression
Acesso em linha:	https://ieeexplore.ieee.org/document/9174730/
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