期刊
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
卷 30, 期 6, 页码 879-890出版社
WILEY
DOI: 10.1002/qre.1688
关键词
support vector data description; one-class classification; density; novelty detection; data mining
资金
- Brain Korea PLUS, Basic Science Research Program through National Research Foundation of Korea - Ministry of Science, ICT and Future Planning [2013007724]
- Ministry of Knowledge Economy in Korea under IT R&D Infrastructure Program [NIPA-2011-(B1110-1101-0002)]
- Ministry of Public Safety & Security (MPSS), Republic of Korea [I2218-13-1004] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [22A20130012646] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
In novelty detection, support vector data description (SVDD) is a one-class classification technique that constructs a boundary to differentiate novel from normal patterns. However, boundaries constructed by SVDD do not consider the density of the data. Data points located in low density regions are more likely to be novel patterns because they are remote from their neighbors. This study presents a density-focused SVDD (DFSVDD), for which its boundary considers both shape and the dense region of the data. Two distance measures, the kernel distance and the density distance, are combined to construct the DFSVDD boundary. The kernel distance can be obtained by solving a quadratic optimization, while support vectors are used to obtain the density distance. A simulation study was conducted to evaluate the performance of the proposed DFSVDD and was then compared with the traditional SVDD. The proposed method performed better than SVDD in terms of the area under the receiver operating characteristic curve. Copyright (c) 2014 John Wiley & Sons, Ltd.
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