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发布时间：2019-03-09

论文类型：期刊论文

发表时间：2010-07-01

发表刊物：NONLINEAR DYNAMICS

收录刊物：Scopus、EI、SCIE

卷号：61

期号：1-2

页面范围：303-310

ISSN号：0924-090X

关键字：Outlier detection; Particle swarm optimization; Support vector machine; Anomaly detection; One-class classification

摘要：Anomalies are patterns in data that do not conform to a well-defined notion of normal behavior. One-class Support Vector Machines calculate a hyperplane in the feature space to distinguish anomalies, but the false positive rate is always high and parameter selection is a key issue. So, we propose a novel one-class framework for detecting anomalies, which takes the advantages of both boundary movement strategy and the effectiveness of evaluation algorithm on parameters optimization. First, we search the parameters by using a particle swarm optimization algorithm. Each particle suggests a group of parameters, the area under receiver operating characteristic curve is chosen as the fitness of the object function. Second, we improve the original decision function with a boundary movement. After the threshold has been adjusted, the final detection function will bring about a high detection rate with a lower false positive rate. Experimental results on UCI data sets show that the proposed method can achieve better performance than other one class learning schemes.