@inproceedings{97e5ead928724f3dbeef3af3c03dd0a2,
title = "A bootstrap based virtual sample generation method for improving the accuracy of modeling complex chemical processes using small datasets",
abstract = "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.",
keywords = "Extreme Learning Machine, Prediction Model, Small Dataset, Virtual Sample Generation",
author = "Zhu, \{Qun Xiong\} and Gong, \{Hong Fei\} and Yuan Xu and He, \{Yan Lin\}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 6th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2017 ; Conference date: 26-05-2017 Through 27-05-2017",
year = "2017",
month = oct,
day = "13",
doi = "10.1109/DDCLS.2017.8068049",
language = "English",
series = "Proceedings of 2017 IEEE 6th Data Driven Control and Learning Systems Conference, DDCLS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "84--88",
editor = "Mingxuan Sun and Huijun Gao",
booktitle = "Proceedings of 2017 IEEE 6th Data Driven Control and Learning Systems Conference, DDCLS 2017",
address = "United States",
}