Density weighted support vector data description

Myungraee Cha, Jun Seok Kim, Jun Geol Baek

Research output: Contribution to journalArticle

43 Citations (Scopus)

Abstract

One-class classification (OCC) has received a lot of attention because of its usefulness in the absence of statistically-representative non-target data. In this situation, the objective of OCC is to find the optimal description of the target data in order to better identify outlier or non-target data. An example of OCC, support vector data description (SVDD) is widely used for its flexible description boundaries without the need to make assumptions regarding data distribution. By mapping the target dataset into high-dimensional space, SVDD finds the spherical description boundary for the target data. In this process, SVDD considers only the kernel-based distance between each data point and the spherical description, not the density distribution of the data. Therefore, it may happen that data points in high-density regions are not included in the description, decreasing classification performance. To solve this problem, we propose a new SVDD introducing the notion of density weight, which is the relative density of each data point based on the density distribution of the target data using the k-nearest neighbor (k-NN) approach. Incorporating the new weight into the search for an optimal description using SVDD, this new method prioritizes data points in high-density regions, and eventually the optimal description shifts to these regions. We demonstrate the improved performance of the new SVDD by using various datasets from the UCI repository.

Original languageEnglish
Pages (from-to)3343-3350
Number of pages8
JournalExpert Systems With Applications
Volume41
Issue number7
DOIs
Publication statusPublished - 2014 Jun 1

Keywords

  • Density weighted SVDD (DW-SVDD)
  • One-class classification (OCC)
  • Support vector data description (SVDD)
  • k-Nearest neighbor approach

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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