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unsupervised Anomaly Detection via DBSCAN for KPIs Jitters in Network Managements

Computers, Materials & Continua 2020年第2期62卷 917-927页

作者： Haiwen Chen Guang Yu Fang Liu Zhiping Cai Anfeng Liu Shuhui Chen Hongbin Huang Chak Fong Cheang College of Computer Science National University of Defense TechnologyChangshaChina School of Data Science and Computer Science Sun Yat-sen UniversityGuangzhouChina College of Information and Engineering Central South UniversityChangshaChina Faculty of Information Technology Macao University of Science and TechnologyMacao

For many Internet companies,a huge amount of KPIs(e.g.,server CPU usage,network usage,business monitoring data)will be generated every *** to closely monitor various KPIs,and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a great challenge,especially for unlabeled *** generated KPIs can be detected by supervised learning with labeled data,but the current problem is that most KPIs are *** is a time-consuming and laborious work to label anomaly for company *** an unsupervised model to detect unlabeled data is an urgent need at *** this paper,unsupervised learning DBSCAN combined with feature extraction of data has been used,and for some KPIs,its best F-Score can reach about 0.9,which is quite good for solving the current problem.

关键词： Anomaly detection KPIs unsupervised learning algorithm

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WORD SENSE DISAMBIGUATION BASED ON IMPROVED BAYESIAN CLASSIFIERS

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Journal of Electronics(China) 2006年第3期23卷 394-398页

作者： Liu Ting Lu Zhimao Li Sheng Computer Science ＆ Technology School Harbin Institute of Technology Harbin 150001 China Computer Science ＆ Technology School Harbin Engineering University Harbin 150001 China

Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in practical application. In this paper, we perform WSD study based on large scale real-world corpus using two unsupervised learning algorithms based on ±n-improved Bayesian model and Dependency Grammar (DG)-improved Bayesian model. ±n-improved classifiers reduce the window size of context of ambiguous words with close-distance feature extraction method, and decrease the jamming of useless features, thus obviously improve the accuracy, reaching 83.18% (in open test). DG-improved classifier can more effectively conquer the noise effect existing in Naive-Bayesian classifier. Experimental results show that this approach does better on Chinese WSD, and the open test achieved an accuracy of 86.27%.

关键词： Word Sense Disambiguation (WSD) Natural Language Processing (NLP) unsupervised learning algorithm Dependency Grammar (DG) Bayesian classifier

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