MBAN-MLC: a multi-label classification method and its application in automating fault diagnosis. (2018) - Chen, Shijun; Gao, Lin; Liao, Guoqiong
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.