@Article{jbd.2021.016317,
AUTHOR = {Diantao Hu, Cong Zhang, Wenqi Cao, Xintao Lv, Songwu Xie},
TITLE = {Grain Yield Predict Based on GRA-AdaBoost-SVR Model},
JOURNAL = {Journal on Big Data},
VOLUME = {3},
YEAR = {2021},
NUMBER = {2},
PAGES = {65--76},
URL = {http://www.techscience.com/jbd/v3n2/42217},
ISSN = {2579-0056},
ABSTRACT = {Grain yield security is a basic national policy of China, and changes in 
grain yield are influenced by a variety of factors, which often have a complex, 
non-linear relationship with each other. Therefore, this paper proposes a Grey 
Relational Analysis–Adaptive Boosting–Support Vector Regression (GRAAdaBoost-SVR) model, which can ensure the prediction accuracy of the model 
under small sample, improve the generalization ability, and enhance the prediction 
accuracy. SVR allows mapping to high-dimensional spaces using kernel functions, 
good for solving nonlinear problems. Grain yield datasets generally have small 
sample sizes and many features, making SVR a promising application for grain 
yield datasets. However, the SVR algorithm’s own problems with the selection of 
parameters and kernel functions make the model less generalizable. Therefore, the 
Adaptive Boosting (AdaBoost) algorithm can be used. Using the SVR algorithm 
as a training method for base learners in the AdaBoost algorithm. Effectively 
address the generalization capability problem in SVR algorithms. In addition, to 
address the problem of sensitivity to anomalous samples in the AdaBoost 
algorithm, the GRA method is used to extract influence factors with higher 
correlation and reduce the number of anomalous samples. Finally, applying the 
GRA-AdaBoost-SVR model to grain yield forecasting in China. Experiments were 
conducted to verify the correctness of the model and to compare the effectiveness 
of several traditional models applied to the grain yield data. The results show that 
the GRA-AdaBoost-SVR algorithm improves the prediction accuracy, the model 
is smoother, and confirms that the model possesses better prediction performance 
and better generalization ability.},
DOI = {10.32604/jbd.2021.016317}
}