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A new method of diesel fuel brands identification: SMOTE oversampling combined with XGBoost ensemble learning

Shutao Wang,Shiyu Liu,Jingkun Zhang,Xiange Che,Yuanyuan Yuan,Zhifang Wang,D. Kong

Published 2020 in Fuel

ABSTRACT

Abstract Using proper diesel brand is the key to ensure the normal operation of diesel engine. It is even more important to identify the brands of diesel oil effectively. This paper presented a new model of near infrared spectroscopy (NIRS) identification of diesel oil brands that combined Tree-based feature selection, Synthetic Minority Oversampling Technique (SMOTE) and Extreme Gradient Boosting (XGBoost) ensemble learning in order to achieve the goal of high accuracy and rapidity. To further demonstrate the practical effect of the proposed ensemble method, it was compared with a single decision tree (DT) classifier based on classification and regression tree (CART) algorithm. As a result, the recognition rate of Tree-SMOTE-XGBoost model proposed in this paper was 19.33% higher than that of XGBoost model, and 9.25% higher than that of Tree-SMOTE-DT model. More importantly, it can ensure the accuracy of each class under the premise of serious imbalance of classes. The proposed method saves manpower and material resources, and provides a new alternative approach for diesel brands identification.

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