Unsupervised Anomaly Detection via DBSCAN for KPIs Jitters in Network Managements

For many Internet companies,a huge amount of KPIs(e.g.,server CPU usage,network usage,business monitoring data)will be generated every day.How 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 data.The generated KPIs can be detected by supervised learning with labeled data,but the current problem is that most KPIs are unlabeled.That is a time-consuming and laborious work to label anomaly for company engineers.Build an unsupervised model to detect unlabeled data is an urgent need at present.In 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.

WORD SENSE DISAMBIGUATION BASED ON IMPROVED BAYESIAN CLASSIFIERS

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%.

Neuro-heuristic computational intelligence for solving nonlinear pantograph systems

We present a neuro-heuristic computing platform for finding the solution for initial value problems （IVPs） of non- linear pantograph systems based on functional differential equations （P-FDEs） of different orders. In this scheme, the strengths of feed-forward artificial neural networks （ANNs）, the evolutionary computing technique mainly based on genetic algorithms （GAs）, and the interior-point technique （IPT） are exploited. Two types of mathematical models of the systems are constructed with the help of ANNs by defining an unsupervised error with and without exactly satisfying the initial conditions. The design parameters of ANN models are optimized with a hybrid approach GA-IPT, where GA is used as a tool for effective global search, and IPT is incorporated for rapid local convergence. The proposed scheme is tested on three different types oflVPs of P-FDE with orders 1-3 The correctness of the scheme is established by comparison with the existing exact solutions. The accuracy and convergence ofthc proposed scheme are further validated through a large number of numerical experiments by taking different numbers of neurons in ANN models.