Fuzzy Multi-Sphere Support Vector Data Description: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013)

Trung Minh LE, Dat TRAN, Wanli MA

Research output: A Conference proceeding or a Chapter in BookConference contribution

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 publicationPacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013)
EditorsJian Pei, Vincent S Tseny, Longbird Cao, Hiroshi Motoda, Guandong Xu
Place of PublicationBerlin
PublisherSpringer
Pages570-581
Number of pages12
Volume7819
ISBN (Print)9783642374524
DOIs
Publication statusPublished - 2013
Event17th Pacific-Asia Conference on Knowledge Discovery and Data Mining - Gold Coast, Gold Coast, Australia
Duration: 14 Apr 201317 Apr 2013

Conference

Conference17th Pacific-Asia Conference on Knowledge Discovery and Data Mining
CountryAustralia
CityGold Coast
Period14/04/1317/04/13

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