Diantao Hu, Cong Zhang*, Wenqi Cao, Xintao Lv, Songwu Xie
Journal on Big Data, Vol.3, No.2, pp. 65-76, 2021, DOI:10.32604/jbd.2021.016317
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.… More >
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