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

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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Bibliographic Details
Main Authors: Ziyi Chen, Wenbai Liu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Machine learning
forecasting problem
K-means clustering method
chaotic slime mould algorithm
support vector regression
Online Access:https://ieeexplore.ieee.org/document/9174730/

Internet

https://ieeexplore.ieee.org/document/9174730/

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