论文标题
基于合奏学习的方法，用于用于故障型识别的多标签能力文本分类
An Ensemble Learning Based Approach to Multi-label Power Text Classification for Fault-type Recognition
论文作者
论文摘要
随着电力行业中ICT自定义服务（ICT CS）的快速发展，部署的电力CS系统主要依靠客户服务人员的经验来进行故障类型的识别，询问和答复，这使得难以精确地解决用户发布的问题。为了解决这个问题，在本文中，首先，通过组合二进制相关性和梯度增强决策树以辅助故障诊断并提高故障类型识别的准确性，提出了一种称为BR-GBDT的多标签故障文本分类集合方法。其次，对于缺乏Power ICT多标签文本分类的培训集的问题，提出了一种自动方法来构建从存储在Power ICT CS系统中的历史故障文本数据中的培训集。广泛的实验是根据Power ICT CS培训组和一些通用基准培训数据集进行的。实验结果表明，我们的方法优于众所周知的基于合奏学习的方法BR+LR和ML-KNN用于故障文本分类，有效地处理ICT自定义服务文本数据的多标签分类以识别故障类型。
With the rapid development of ICT Custom Services (ICT CS) in power industries, the deployed power ICT CS systems mainly rely on the experience of customer service staff for fault type recognition, questioning, and answering, which makes it difficult and inefficient to precisely resolve the problems issued by users. To resolve this problem, in this paper, firstly, a multi-label fault text classification ensemble approach called BR-GBDT is proposed by combining Binary Relevance and Gradient Boosting Decision Tree for assisted fault type diagnosis and improving the accuracy of fault type recognition. Second, for the problem that there is lack of the training set for power ICT multi-label text classification, an automatic approach is presented to construct the training set from the historical fault text data stored in power ICT CS systems. The extensive experiments were made based on the power ICT CS training set and some general-purpose benchmark training datasets. The experiment results show that our approach outperforms the well known ensemble learning based approaches BR+LR and ML-KNN for fault text classification, efficiently handling the multi-label classification of ICT custom service text data for fault type recognition.
