Novelty detection, also referred to as one-class classification, is the process of detecting 'abnormal' behavior in a system by learning the 'normal' behavior. Novelty detection has been of particular interest to researchers in domains where it is difficult or expensive to find examples of abnormal behavior (such as in medical/equipment diagnosis and IT network surveillance). Effective representation of normal data is of primary interest in pursuing one-class classification. While the literature offers several methods for one-class classification, very few methods can support representation of non-stationary classes without making stringent assumptions about the class distribution. This paper proposes a one-class classification method for non-stationary classes using a modified support vector machine and an efficient online version for reducing computational time. The presented method is applied to several simulated datasets and actual data from a drilling machine. In addition, we present comparison results with other methods that demonstrate its superior performance. (C) 2008 Elsevier Ltd. All rights reserved
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