Ensemble of extreme learning machines for multivariate calibration of near-infrared spectroscopy.

Abstract

Inspired by the attractive features of extreme learning machine (ELM), a simple ensemble ELM algorithm, named EELM, is proposed for multivariate calibration of near-infrared spectroscopy. Such an algorithm takes full advantage of random initialization of the weights of the hidden layer in ELM for obtaining the diversity between member models. Also, by combining a large number of member models, the stability of the final prediction can be greatly improved and the ensemble model outperforms its best member model. Compared with partial least-squares (PLS), the superiority of EELM is attributed to its inherent characteristics of high learning speed, simple structure and excellent predictive performance. Three NIR spectral datasets concerning solid samples are used to verify the proposed algorithm in terms of both the accuracy and robustness. The results confirmed the superiority of EELM to classic PLS. Also, even if the experiment is done on NIR datasets, it provides a good reference for other spectral calibration.