MBAN-MLC: a multi-label classification method and its application in automating fault diagnosis. (2018)

• Record Type: Journal Article
• Authors: Chen, Shijun; Gao, Lin; Liao, Guoqiong
• Is Part Of: International journal of internet manufacturing and services. Volume 5:Number 4(2018)
• Page Start: 350
• Page End: 364
• Publication Date: 2018
• Subjects: multi-label classification -- multi-label learning -- Bayesian network -- Bayesian network-augmented naïve Bayes -- multiple classifying nodes -- fault diagnosis; Computer integrated manufacturing systems -- Periodicals; Internet -- Periodicals; 670.2854678
• Languages: English
• ISSNs: 1751-6048

Abstract

With increasing competition in the mobile telecom network market, the network quality becomes one of the key factors for competitions between network operators. At the work site of drive-testing in the mobile radio communication network, drive-testing experts needed to tag the fault causes manually based on their experience for fault diagnosis, and there were always multiple causes. Traditional single-label classification method cannot be used here to automatically tag the multiple fault causes. In this paper such kind of fault diagnosis problem is transformed to the multi-label classification problem, and a multi-label classification method (MBAN-MLC) is proposed for fault diagnosis automation. MBAN-MLC is based on a Bayesian network-augmented naïve Bayes model with multiple classifying nodes. In the MBAN-MLC method the relationship between labels are taken into considered to improve the classification precision during the model construction and inference. The MBAN-MLC method is also verified to be effective in the proprietary drive-testing fault diagnosis dataset and standard multi-label dataset, and does improve the efficiency of fault diagnosis of drive-testing greatly in contrast to traditional manual mode.