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首页> 外文期刊>International Journal of Internet Manufacturing and Services >MBAN-MLC: a multi-label classification method and its application in automating fault diagnosis
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MBAN-MLC: a multi-label classification method and its application in automating fault diagnosis

机译：MBAN-MLC：多标签分类方法及其在自动诊断中的应用

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摘要

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 na?ve 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.
机译：随着移动电信网络市场竞争的增加，网络质量成为网络运营商竞争的关键因素之一。在移动无线电通信网络中的驱动测试的工作现场，驱动测试专家需要根据其故障诊断的体验手动标记故障原因，并且总是有多种原因。这里不能使用传统的单标分类方法来自动标记多个故障原因。在本文中，这种故障诊断问题被转换为多标签分类问题，并且提出了一种用于故障诊断自动化的多标签分类方法（MBAN-MLC）。 MBAN-MLC基于贝叶斯网络增强NA澳门贝叶斯模型，具有多个分类节点。在MBAM-MLC方法中，标签之间的关系被认为是在模型构造和推理过程中提高分类精度。还验证了MBAN-MLC方法在专有的驱动测试故障诊断数据集和标准多标签数据集中是有效的，并且与传统的手动模式相比，提高了大大概率诊断的效率。

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