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Indexed by:期刊论文

Date of Publication:2011-04-01

Journal:NONLINEAR DYNAMICS

Included Journals:Scopus、SCIE、EI

Volume:64

Issue:1-2

Page Number:127-130

ISSN No.:0924-090X

Key Words:One class support vector machine; Anomaly detection; Local density degree

Abstract: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, however, it may not identify the ideal hyperplane especially when the support vectors do not have the overall characteristics of the target data. So, we propose a new local density OCSVM by incorporating distance measurements based on local density degree to reflect the distribution of a given data set. Experimental results on UCI data sets show that the proposed method can achieve better performance than other one class learning schemes.