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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Acknowledgements
This work is supported by the National Natural Science Foundation of China under Grant Nos.61976216 and 61672522.
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Yuting Sun declares that she has no conflict of interest. Shifei Ding declares that he has no conflict of interest. Zichen Zhang declares that he has no conflict of interest. Weikuan Jia declares that he has no conflict of interest.
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