A Multi-label Classification Approach for ICT Fault Text Analysis

被引：0
|
作者：
Zhang, Qiang [1 ]
Chen, Xiaona [2 ]
机构：
[1] Global Energy Interconnect Res Inst Co Ltd, Artificial Intelligence Elect Power Syst State Gr, WeiLaiKeJiCheng Pk, Beijing 102209, Peoples R China
[2] North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China
来源：
2019 12TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2019) | 2019年
关键词：
electric power fault diagnosis; multi-label text classification; Binary Relevance; Gradient Boosting;
D O I：
10.1109/ISCID.2019.10138
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
The ICT customer service from State Grid has accumulated a large number of electric power fault reports and texts, which are difficult to classify and analyze because an occurrence of power fault may be triggered possibly by multiple different causes. In order to track different causes of electric power faults from historical fault texts, a multi-label based text classification approach is proposed for fault-assisted decision. Firstly, Chinese fault texts are sequentially preprocessed by word segmentation, stop words removal and feature representation. Then, we present a multi-label text classification by combining Binary Relevance and Gradient Boosting algorithm. The experimental results show that our method is better than BR+LR and ML-KNN for fault text classification.
引用
收藏
页码：241 / 244
页数：4
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