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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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