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 SVDDdo 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.
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Management Science and Operations Research