Fuzzy multi-sphere support vector data description

Trung Le, Dat Tran, Wanli Ma

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

6 Citations (Scopus)

Abstract

Current well-known data description methods such as Support Vector Data Description and Small Sphere Large Margin are conducted with assumption that data samples of a class in feature space are drawn from a single distribution. Based on this assumption, a single hypersphere is constructed to provide a good data description for the data. However, real-world data samples may be drawn from some distinctive distributions and hence it does not guarantee that a single hypersphere can offer the best data description. In this paper, we introduce a Fuzzy Multi-sphere Support Vector Data Description approach to address this issue. We propose to use a set of hyperspheres to provide a better data description for a given data set. Calculations for determining optimal hyperspheres and experimental results for applying this proposed approach to classification problems are presented.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings
PublisherSpringer
Pages570-581
Number of pages12
EditionPART 2
ISBN (Print)9783642374555
DOIs
Publication statusPublished - 1 Dec 2013
Externally publishedYes
EventPacific-Asia Conference on Knowledge Discovery and Data Mining 2013 - Gold Coast, Australia
Duration: 14 Apr 201317 Apr 2013
Conference number: 17th
https://link.springer.com/book/10.1007/978-3-642-37453-1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7819 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 2013
Abbreviated titlePAKDD 2013
CountryAustralia
CityGold Coast
Period14/04/1317/04/13
Internet address

Keywords

  • Fuzzy interference
  • Kernel methods
  • Multi-Sphere support vector data description
  • Support vector data description

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