A Novel Interpretable Ensemble Learning Method for NIR-Based Rapid Characterization of Petroleum Products
Huijing Yu, Yuqiang Li, Wenli Du, Minglei Yang, Xin Peng, Xinjie Wang, Jian Long
Abstract
Near-Infrared (NIR) spectroscopy has become an important analytical tool to perform rapid characterization of petroleum products due to its effectiveness and efficiency. However, NIR analysis is a typical small sample problem and unitary regression model under specific assumption often suffers from poor generalization ability during online implementation. Besides, the interpretability of complex machine learning models is relative incompetent to reveal the variable contribution of wavelength variables, which is significant to guarantee the model reliability in practical application. Thus, this paper proposes a novel ensemble learning algorithm to improve the poor model generalization ability caused by insufficient sample size and complex unexpected online application situations. Furthermore, the constructed model is interpreted by combining model agnostic explanation method with spectral structure knowledge and a novel reliability index is proposed to ensure model reliability in practice. The prediction performance of proposed algorithm is verified using two industrial NIR dataset. Experimental results confirm the effectiveness of proposed ensemble learning algorithm. The model interpretability as well as model reliability are guaranteed by proposed interpretation method.