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Soft Computing
Published in: Soft Computing 7/2021

20-01-2021 | Methodologies and Application

An improved grid search algorithm to optimize SVR for prediction

Authors: Yuting Sun, Shifei Ding, Zichen Zhang, Weikuan Jia

Published in: Soft Computing | Issue 7/2021

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Abstract

Parameter optimization is an important step for support vector regression (SVR), since its prediction performance greatly depends on values of the related parameters. To solve the shortcomings of traditional grid search algorithms such as too many invalid search ranges and sensitivity to search step, an improved grid search algorithm is proposed to optimize SVR for prediction. The improved grid search (IGS) algorithm is used to optimize the penalty parameter and kernel function parameter of SVR by automatically changing the search range and step for several times, and then SVR is trained for the optimal solution. The available of the method is proved by predicting the values of soil and plant analyzer development (SPAD) in rice leaves. To predict SPAD values more quickly and accurately, some dimension reduction methods such as stepwise multiple linear regressions (SMLR) and principal component analysis (PCA) are processed the training data, and the results show that the nonlinear fitting and prediction performance of accuracy of SMLR-IGS-SVR and PCA-IGS-SVR are better than those of IGS-SVR.
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Metadata
Title
An improved grid search algorithm to optimize SVR for prediction
Authors
Yuting Sun
Shifei Ding
Zichen Zhang
Weikuan Jia
Publication date
20-01-2021
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 7/2021
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-020-05560-w

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