General support vector representation machine for one-class classification of non-stationary classes
Pattern RecognitionPublished 7 April 2008
Fatih Camci, Ratna Babu Chinnam
Citations55
SJR quartileQ1
SJR score2.06
SNIP2.67
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TL;DR
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.
Abstract
Novelty detection, also referred to as one-class classification, is the process \nof detecting 'abnormal' behavior in a system by learning the 'normal' behavior. \nNovelty detection has been of particular interest to researchers in domains \nwhere it is difficult or expensive to find examples of abnormal behavior (such \nas in medical/equipment diagnosis and IT network surveillance). Effective \nrepresentation of normal data is of primary interest in pursuing one-class \nclassification. While the literature offers several methods for one-class \nclassification, very few methods can support representation of non-stationary \nclasses without making stringent assumptions about the class distribution. This \npaper proposes a one-class classification method for non-stationary classes \nusing a modified support vector machine and an efficient online version for \nreducing computational time. The presented method is applied to several \nsimulated datasets and actual data from a drilling machine. In addition, we \npresent comparison results with other methods that demonstrate its superior \nperformance. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords
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