Fault recognition using an ensemble classifier based on Dempster–Shafer Theory. (March 2020)

• Record Type: Journal Article
• Authors: Wang, Zhen; Wang, Rongxi; Gao, Jianmin; Gao, Zhiyong; Liang, Yanjie
• Is Part Of: Pattern recognition. Volume 99(2020:Mar.)
• Publication Date: 2020-03
• Subjects: Fault recognition -- Ensemble classifier -- Dempster–Shafer Theory -- Correlation entropy -- Evidence weight; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.107079
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose an effective combination method based on Dempster–Shafer Theory for ensemble classifiers. The proposed method measures the outputs of member classifiers using a combinational weight. The combinational weight consists of objective weight and subjective weight. The objective weight is defined based on the support degree among classifiers. The subjective weight is defined based on support degree of within classifier. Abstract: Aiming at the poor performance of individual classifier in the field of fault recognition, in this paper, a new ensemble classifier is constructed to improve the classification accuracy by combining multiple classifiers based on Dempster–Shafer Theory (DST). However, in some specific cases, especially when dealing with the combination of conflicting evidences, the DST may produce counter-intuitive results and loss its advantages in combining classifiers. To solve this problem, a new improved combination method is proposed to alleviate the conflicts between evidences and a new ensemble technique is developed for the combination of individual classifiers, which can be well used in the design of accurate classifier ensembles. The main advantage of the proposed combination method is that of making the combination process more efficient and accurate by defining the objective weights and subjective weights of member classifiers' outputs. To verify the effectiveness of the proposed combination method, four individual classifiers are selected for constructing ensemble classifier and tested on Tennessee-Eastman Process (TEP) datasets and UCI machine learning datasets. The experimental results show that the ensemble classifier can significantly improve the classification accuracy and outperforms all the selected individual classifiers. By comparison with other combination methods based on DST and some state-of-the-art ensemble methods, the proposed combination method shows better abilities in dealing with the combination of individual classifiers and outperforms the others in multiple performance measurements. Finally, the proposed ensemble classifier is applied to the fault recognition in real chemical plant.