[1]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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Research on communication network fault classification based on data mining

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 6 Page number: 1228-1234 Column: 学术论文—自然语言处理与理解 Public date: 2022-11-05
Title:
Research on communication network fault classification based on data mining
Author(s):
ZHU Zhen1;  LIU Lifang1;  QI Xiaogang2
1. School of Computer Science and Technology, Xidian University, Xi’an 710071, China;
2. School of Mathematics and Statistics, Xidian University, Xi’an 710071, China
Keywords:
data mining; communication network; fault classification; integrated learning; characteristic engineering; alarm data; network failure; network alarm
CLC:
TP181
DOI:
10.11992/tis.202111037
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.
References:
[1] WANG Yang, LI Guocai, XU Yakun, et al. An algorithmfor mining of association rules for the information communication network alarms based on swarm intelligence[J]. Mathematical problems in engineering, 2014, 2014: 1–14.
[2] WANG Yang, XU Yakun, LI Gang, et al. Association rules mining algorithm using sharing mechanism niche leaping ant colony based on apriori algorithm[J]. Journal of computational and theoretical nanoscience, 2015, 12(10): 3223–3231.
[3] LI Chuan, HUANG Xiaojun. Research on FP-Growth algorithm for massive telecommunication network alarm data based on Spark[C]//2016 7th IEEE International Conference on Software Engineering and Service Science. Beijing: IEEE, 2016: 875-879.
[4] WANG Jiantao, HE Caifeng, LIU Yijun, et al. Efficient alarm behavior analytics for telecom networks[J]. Information sciences, 2017, 402: 1–14.
[5] 罗明, 孟传伟, 黄海量. 基于加权频繁模式树的通信网络告警规则挖掘方法[J]. 计算机工程, 2016, 42(4): 190–196
LUO Ming, MENG Chuanwei, HUANG Hailiang. Alarm rule mining method in telecommunication network based on weighted frequent pattern-tree[J]. Computer engineering, 2016, 42(4): 190–196
[6] AGRAWAL R, SRIKANT R. Mining sequential patterns[C]//Proceedings of the eleventh international conference on data engineering. Taipei: IEEE, 1995: 3-14.
[7] WANG Jia, LI Hongguang, HUANG Jingwen, et al. Association rules mining based analysis of consequential alarm sequences in chemical processes[J]. Journal of loss prevention in the process industries, 2016, 41: 178–185.
[8] LIM H K, KIM Y, KIM M K. Failure prediction using sequential pattern mining in the wire bonding process[J]. IEEE transactions on semiconductor manufacturing, 2017, 30(3): 285–292.
[9] STEINDER M, SETHI A S. End-to-end service failure diagnosis using belief networks[C]// NOMS 2002. IEEE/IFIP Network Operations and Management Symposium. Management Solutions for the New Communications World’ (Cat. No. 02CH37327). Florence: IEEE, 2002: 375-390.
[10] CHAN A, MCNAUGHT K R. Using Bayesian networks to improve fault diagnosis during manufacturing tests of mobile telephone infrastructure[J]. Journal of the operational research society, 2008, 59(4): 423–430.
[11] 王开选, 杨峥, 邱雪松. 面向影响分析的电力通信网故障定位算法[J]. 北京邮电大学学报, 2014, 37(S1): 55–59
WANG Kaixuan, YANG Zheng, QIU Xuesong. Algorithm of fault locating on impact analysis in power communications network[J]. Journal of Beijing University of Posts and Telecommunications, 2014, 37(S1): 55–59
[12] SHI Jingjing, YANG Lin, ZHU Axing, et al. Machine-learning variables at different scales vs. knowledge-based variables for mapping multiple soil properties[J]. Soil science society of America journal, 2018, 82(3): 645–656.
[13] 王迎春, 王远波, 王平. 基于规则的通信网故障自动诊断系统[J]. 计算机与现代化, 2010(2): 22–25
WANG Yingchun, WANG Yuanbo, WANG Ping. Rule-based automatic fault diagnosis system for communication networks[J]. Computer and modernization, 2010(2): 22–25
[14] 赵灿明, 李祝红, 陶磊, 等. 基于故障传播模型与监督学习的电力通信网络故障定位[J]. 计算机应用, 2016, 36(4): 905–908,922
ZHAO Canming, LI Zhuhong, TAO Lei, et al. Fault localization for electric power communication network based on fault propagation model and supervised learning[J]. Journal of computer applications, 2016, 36(4): 905–908,922
[15] 王菲. 基于深度学习的光网络故障定位技术研究[D]. 北京: 北京邮电大学, 2019.
WANG Fei. Research of fault location in optical networks based on deep learning[D]. Beijing: Beijing University of Posts and Telecommunications, 2019.
[16] 张曾莲. 基于非营利性、数据挖掘和科学管理的高校财务分析、评价与管理研究[M]. 北京: 首都经济贸易大学出版社, 2014.
[17] 张银杰, 揣锦华, 翟晓惠. 基于特征工程与集成学习的恶意软件预测研究[J]. 计算机时代, 2020(7): 7–11
ZHANG Yinjie, CHUAI Jinhua, ZHAI Xiaohui. Research on malware prediction based on feature engineering and ensemble learning[J]. Computer era, 2020(7): 7–11
[18] 胡毅, 瞿博阳, 梁静, 等. 进化集成学习算法综述[J]. 智能科学与技术学报, 2021, 3(1): 18–33
HU Yi, QU Boyang, LIANG Jing, et al. A survey on evolutionary ensemble learning algorithm[J]. Chinese journal of intelligent science and technology, 2021, 3(1): 18–33
[19] 汪星, 黄小瑜, 刘瑄璞, 等. 面向工业大数据的多层增量特征提取方法[J]. 西安电子科技大学学报, 2018, 45(4): 106–111
WANG Xing, HUANG Xiaoyu, LIU Xuanpu, et al. Multi-layer incremental feature extraction method for industrial big data[J]. Journal of Xidian University, 2018, 45(4): 106–111
[20] WANG Jing, WANG Huaichao, ZHANG Jiyang. Alarm association rules mining based on Run log for civil aviation information system[C]//2017 8th IEEE International Conference on Software Engineering and Service Science. Beijing: IEEE, 2017: 836-841.
[21] CHEN Kuoming, CHANG T H, WANG Kaicheng, et al. Machine learning based automatic diagnosis in mobile communication networks[J]. IEEE transactions on vehicular technology, 2019, 68(10): 10081–10093.
[22] JI Xilin, SHI Xiaodan, HAN Jinxi, et al. The alarm feature analysis algorithm for communication network[M]//Proceedings of the 9th International Conference on Computer Engineering and Networks. Singapore: Springer Singapore, 2020: 255-265.
[23] WANG Danshi, LOU Liqi, ZHANG Min, et al. Dealing with alarms in optical networks using an intelligent system[J]. IEEE access, 2019, 7: 97760–97770.
[24] RUIZ M, FRESI F, VELA A P, et al. Service-triggered failure identification/localization through monitoring of multiple parameters[C]//ECOC 2016: 42nd European Conference on Optical Communication. Düsseldorf: VDE, 2016: 1-3.
[25] KE G, MENG Q, FINLEY T, et al. Lightgbm: a highly efficient gradient boosting decision tree[J]. Advances in neural information processing systems, 2017, 30: 3146–3154.
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