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

    5 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

    Fingerprint

    Data description
    Data mining

    Cite this

    LE, T. M., TRAN, D., & MA, W. (2013). Fuzzy Multi-Sphere Support Vector Data Description: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013). In J. Pei, V. S. Tseny, L. Cao, H. Motoda, & G. Xu (Eds.), Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013) (Vol. 7819, pp. 570-581). Berlin: Springer. https://doi.org/10.1007/978-3-642-37456-2_48
    LE, Trung Minh ; TRAN, Dat ; MA, Wanli. / Fuzzy Multi-Sphere Support Vector Data Description : Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013). Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013). editor / Jian Pei ; Vincent S Tseny ; Longbird Cao ; Hiroshi Motoda ; Guandong Xu. Vol. 7819 Berlin : Springer, 2013. pp. 570-581
    @inproceedings{444ad92fc9ab455aac69e284db40c076,
    title = "Fuzzy Multi-Sphere Support Vector Data Description: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013)",
    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",
    keywords = "Support Vector Data Description, Kernel Method, EEG",
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    editor = "Jian Pei and Tseny, {Vincent S} and Longbird Cao and Hiroshi Motoda and Guandong Xu",
    booktitle = "Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013)",
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    LE, TM, TRAN, D & MA, W 2013, Fuzzy Multi-Sphere Support Vector Data Description: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013). in J Pei, VS Tseny, L Cao, H Motoda & G Xu (eds), Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013). vol. 7819, Springer, Berlin, pp. 570-581, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Gold Coast, Australia, 14/04/13. https://doi.org/10.1007/978-3-642-37456-2_48

    Fuzzy Multi-Sphere Support Vector Data Description : Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013). / LE, Trung Minh; TRAN, Dat; MA, Wanli.

    Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013). ed. / Jian Pei; Vincent S Tseny; Longbird Cao; Hiroshi Motoda; Guandong Xu. Vol. 7819 Berlin : Springer, 2013. p. 570-581.

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

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    T1 - Fuzzy Multi-Sphere Support Vector Data Description

    T2 - Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013)

    AU - LE, Trung Minh

    AU - TRAN, Dat

    AU - MA, Wanli

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    N2 - 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

    AB - 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

    KW - Support Vector Data Description

    KW - Kernel Method

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    BT - Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013)

    A2 - Pei, Jian

    A2 - Tseny, Vincent S

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    A2 - Motoda, Hiroshi

    A2 - Xu, Guandong

    PB - Springer

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    LE TM, TRAN D, MA W. Fuzzy Multi-Sphere Support Vector Data Description: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013). In Pei J, Tseny VS, Cao L, Motoda H, Xu G, editors, Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013). Vol. 7819. Berlin: Springer. 2013. p. 570-581 https://doi.org/10.1007/978-3-642-37456-2_48