[1]朱圳,刘立芳,齐小刚.基于数据挖掘的通信网络故障分类研究[J].智能系统学报,2022,17(6):1228-1234.[doi:10.11992/tis.202111037]
ZHU Zhen,LIU Lifang,QI Xiaogang.Research on communication network fault classification based on data mining[J].CAAI Transactions on Intelligent Systems,2022,17(6):1228-1234.[doi:10.11992/tis.202111037]
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ZHU Zhen,LIU Lifang,QI Xiaogang.Research on communication network fault classification based on data mining[J].CAAI Transactions on Intelligent Systems,2022,17(6):1228-1234.[doi:10.11992/tis.202111037]
基于数据挖掘的通信网络故障分类研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第6期
页码:
1228-1234
栏目:
学术论文—自然语言处理与理解
出版日期:
2022-11-05
- Title:
- Research on communication network fault classification based on data mining
- Keywords:
- data mining; communication network; fault classification; integrated learning; characteristic engineering; alarm data; network failure; network alarm
- 分类号:
- TP181
- 文献标志码:
- 2022-10-14
- 摘要:
- 以往针对通信网络故障分类的算法没有考虑告警和故障数据中的潜在特征，导致故障分类准确率低，因此提出一种基于数据挖掘的通信网络故障分类算法。首先，根据对数据背景和数据特点的理解，使用特征构造挖掘数据中潜在的特征，将挖掘到的特征加入原数据中。然后，使用LightGBM算法的特征重要性评估函数对新数据集中的所有特征进行重要性评估，根据重要性值删除不重要特征。最后，使用集成学习模型对特征筛选后的数据集进行故障分类研究。实验结果表明，基于数据挖掘的通信网络故障分类算法的准确率有更好的效果。
- Abstract:
- The communication network fault classification algorithms previously used did not consider potential features in alarm and fault data, resulting in low classification accuracy. This study proposes a data mining-based communication network fault classification algorithm to address this issue. First, the feature structure is applied to the mining of the potential features in the data based on the understanding of the data background and data characteristics, and then the mined features are added to the original data. Furthermore, the LightGBM algorithm’s feature importance evaluation function is used to evaluate the importance of all features in the new dataset and to delete unimportant features based on the importance value. Finally, ensemble learning algorithms are used to perform fault classification studies on feature-screened datasets. The experimental results show that the accuracy of the communication network fault classification algorithm based on data mining has a better effect.
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备注/Memo
收稿日期:2021-11-19。
基金项目:国家自然科学基金项目(61877067).
作者简介:朱圳，硕士研究生，主要研究方向为数据处理与分析、数据挖掘和通信网络故障;刘立芳，教授，博士，主要研究方向为数据处理与智能计算。主持国家自然科学基金项目、陕西省自然科学基金项目等国家和省部级项目5项。发表学术论文40篇;齐小刚，教授，博士生导师，博士，主要研究方向为复杂系统建模与仿真、网络算法设计与应用。主持国家自然科学基金项目、十三五预研项目等国家和省部级项目20余项。获授权专利19项，软件著作权4项。发表学术论文100余篇
通讯作者:刘立芳.E-mail:lfliu@mail.xidian.edu.cn
更新日期/Last Update:
1900-01-01