Rapid determination of hemoglobin concentration by a novel ensemble extreme learning machine method combined with near-infrared spectroscopy
作者：Kaiyi Wang, Xihui Bian*, Meng Zheng, Peng Liu, Ligang Lin, Xiaoyao Tan
關鍵字：Extreme learning machine, Monte Carlo sampling, Least absolute shrinkage and selection operator, Multivariate calibration, Hemoglobin concentration
論文來源：期刊
具體來源：Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 263, 120138
發表時間：2021年
A novel ensemble extreme learning machine (ELM) approach that combines Monte Carlo (MC) sampling and least absolute shrinkage and selection operator (LASSO), named as MC-LASSO-ELM, is proposed to determine hemoglobin concentration of blood. It employs MC sampling to randomly select samples from the training set and LASSO further to choose variables from selected samples to establish plenty of ELM sub-models. The final prediction is obtained by combining the predictions of these sub-models. Combined with near-infrared spectroscopy, MC-LASSO-ELM is used to determine the hemoglobin concentration of blood. Compared with ELM, MC-ELM and LASSO-ELM, MC-LASSO-ELM can obtain the best stability and highest accuracy