A bootstrap based virtual sample generation method for improving the accuracy of modeling complex chemical processes using small datasets

Though in the era of big data, it remains a challenge to be tackled that the forecasting model with high accuracy and robustness needs to be built using small size samples. One effective tool of addressing this problem is the virtual sample generation (VSG), which can generate a mass of new virtual samples on the basis of small sample sets. The bootstrap method is adopted to feasibly resample the virtual samples in this paper. The effectiveness of the proposed bootstrap virtual sample generation (BVSG) is evaluated over one real case. The experimental results show that the proposed approach achieves better performance with the aid of virtual samples.