Fuzzy Entropy Semi-Supervised Support Vector Data Description

Trung Minh LE, Dat TRAN, Tien Tran, Khanh Nyugen, Wanli MA

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

    5 Citations (Scopus)

    Abstract

    Support Vector Data Description (SVDD) is known as one of the best kernel-based methods for one-class classification problems. SVDD requires fully labelled data sets. However, in reality, an abundant amount of data can be easily collected, while the labelling process is often expensive, time-consuming, and error-prone. Therefore, partially labelled data sets are popular and easy to obtain. In this paper, we propose a semi-supervised learning method, Fuzzy Entropy Semi-supervised SVDD (FS3VDD), to extend SVDD to cope with partially labelled data sets. The learning model employs fuzzy membership and fuzzy entropy to help the labelling of the unlabeled data.
    Original languageEnglish
    Title of host publicationThe 2013 International Joint Conference on Neural Networks (IJCNN)
    EditorsPlaman Angelov, Daniel Levine
    Place of PublicationUSA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages2339-2343
    Number of pages5
    Volume1
    ISBN (Print)9781467361293
    DOIs
    Publication statusPublished - 2013
    Event2013 International Joint Conference on Neural Networks (IJCNN) - Dallas, Texas, United States
    Duration: 4 Aug 20139 Aug 2013

    Conference

    Conference2013 International Joint Conference on Neural Networks (IJCNN)
    CountryUnited States
    CityTexas
    Period4/08/139/08/13

    Fingerprint

    Data description
    Entropy
    Labeling
    Supervised learning

    Cite this

    LE, T. M., TRAN, D., Tran, T., Nyugen, K., & MA, W. (2013). Fuzzy Entropy Semi-Supervised Support Vector Data Description. In P. Angelov, & D. Levine (Eds.), The 2013 International Joint Conference on Neural Networks (IJCNN) (Vol. 1, pp. 2339-2343). USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2013.6707033
    LE, Trung Minh ; TRAN, Dat ; Tran, Tien ; Nyugen, Khanh ; MA, Wanli. / Fuzzy Entropy Semi-Supervised Support Vector Data Description. The 2013 International Joint Conference on Neural Networks (IJCNN). editor / Plaman Angelov ; Daniel Levine. Vol. 1 USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 2339-2343
    @inproceedings{fbbbb71f184a4257bf59f6d9cdea68d3,
    title = "Fuzzy Entropy Semi-Supervised Support Vector Data Description",
    abstract = "Support Vector Data Description (SVDD) is known as one of the best kernel-based methods for one-class classification problems. SVDD requires fully labelled data sets. However, in reality, an abundant amount of data can be easily collected, while the labelling process is often expensive, time-consuming, and error-prone. Therefore, partially labelled data sets are popular and easy to obtain. In this paper, we propose a semi-supervised learning method, Fuzzy Entropy Semi-supervised SVDD (FS3VDD), to extend SVDD to cope with partially labelled data sets. The learning model employs fuzzy membership and fuzzy entropy to help the labelling of the unlabeled data.",
    keywords = "Fuzzy Entropy, Support Vector Data Description",
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    LE, TM, TRAN, D, Tran, T, Nyugen, K & MA, W 2013, Fuzzy Entropy Semi-Supervised Support Vector Data Description. in P Angelov & D Levine (eds), The 2013 International Joint Conference on Neural Networks (IJCNN). vol. 1, IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 2339-2343, 2013 International Joint Conference on Neural Networks (IJCNN), Texas, United States, 4/08/13. https://doi.org/10.1109/IJCNN.2013.6707033

    Fuzzy Entropy Semi-Supervised Support Vector Data Description. / LE, Trung Minh; TRAN, Dat; Tran, Tien; Nyugen, Khanh; MA, Wanli.

    The 2013 International Joint Conference on Neural Networks (IJCNN). ed. / Plaman Angelov; Daniel Levine. Vol. 1 USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 2339-2343.

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

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    N2 - Support Vector Data Description (SVDD) is known as one of the best kernel-based methods for one-class classification problems. SVDD requires fully labelled data sets. However, in reality, an abundant amount of data can be easily collected, while the labelling process is often expensive, time-consuming, and error-prone. Therefore, partially labelled data sets are popular and easy to obtain. In this paper, we propose a semi-supervised learning method, Fuzzy Entropy Semi-supervised SVDD (FS3VDD), to extend SVDD to cope with partially labelled data sets. The learning model employs fuzzy membership and fuzzy entropy to help the labelling of the unlabeled data.

    AB - Support Vector Data Description (SVDD) is known as one of the best kernel-based methods for one-class classification problems. SVDD requires fully labelled data sets. However, in reality, an abundant amount of data can be easily collected, while the labelling process is often expensive, time-consuming, and error-prone. Therefore, partially labelled data sets are popular and easy to obtain. In this paper, we propose a semi-supervised learning method, Fuzzy Entropy Semi-supervised SVDD (FS3VDD), to extend SVDD to cope with partially labelled data sets. The learning model employs fuzzy membership and fuzzy entropy to help the labelling of the unlabeled data.

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    LE TM, TRAN D, Tran T, Nyugen K, MA W. Fuzzy Entropy Semi-Supervised Support Vector Data Description. In Angelov P, Levine D, editors, The 2013 International Joint Conference on Neural Networks (IJCNN). Vol. 1. USA: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 2339-2343 https://doi.org/10.1109/IJCNN.2013.6707033