Fuzzy multi-sphere support vector data description

Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)

Abstract

Multi-sphere Support Vector Data Description (MS-SVDD) has been proposed in our previous work. MS-SVDD aims to build a set of spherically shaped boundaries that provide a better data description to the normal dataset and an iterative learning algorithm that determines the set of spherically shaped boundaries. MS-SVDD could improve classification rate for one-class classification problems comparing with SVDD. However MS-SVDD requires a small abnormal data set to build the spherically shaped boundaries for the normal data set. In this paper, we propose a new fuzzy MS-SVDD that can be used when only the normal data set is available. Experimental results on 14 well-known datasets and a comparison between fuzzy MS-SVDD and SVDD are also presented.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012
ISBN (Electronic)9781467315050
DOIs
Publication statusPublished - 2012
Externally publishedYes
EventIEEE International Conference on Fuzzy Systems 2012 - Brisbane, Australia
Duration: 10 Jun 201215 Jun 2012
Conference number: 21st
https://ieeexplore.ieee.org/xpl/conhome/6241469/proceeding (Proceedings)

Conference

ConferenceIEEE International Conference on Fuzzy Systems 2012
Abbreviated titleFUZZ-IEEE 2012
CountryAustralia
CityBrisbane
Period10/06/1215/06/12
Internet address

Keywords

  • fuzzy model
  • Novelty detection
  • one-class classification
  • support vector data description

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