A new fuzzy membership computation method for fuzzy support vector machines

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

    3 Citations (Scopus)

    Abstract

    Support vector machine (SVM) considers all data points with the same importance in classification problems, therefore SVM is very sensitive to noisy data or outliers. Current fuzzy approach to two-class SVM introduces a fuzzy membership to each data point in order to reduce the sensitivity of less important data, however computing fuzzy memberships is still a challenge. It has been found that the performance of fuzzy SVM highly depends on the computation of fuzzy memberships, hence in this paper, we propose a new method to compute fuzzy memberships and we also extend the fuzzy approach for two-class SVM to one-class SVM. Experiments performed on a number of popular data sets to evaluation the proposed fuzzy SVMs show promising classification results.
    Original languageEnglish
    Title of host publication2010 Third International Conference on Communications and Electronics (ICCE)
    Place of PublicationUSA
    PublisherIEEE
    Pages153-157
    Number of pages5
    ISBN (Print)9781424470570
    DOIs
    Publication statusPublished - 2010
    EventICCE 2010: The Third International Conference on Communications and Electronics - Nha Trang, Viet Nam
    Duration: 11 Aug 201013 Aug 2010

    Conference

    ConferenceICCE 2010: The Third International Conference on Communications and Electronics
    CountryViet Nam
    CityNha Trang
    Period11/08/1013/08/10

    Fingerprint

    Support vector machines
    Experiments

    Cite this

    Tran, D., Ma, W., & Sharma, D. (2010). A new fuzzy membership computation method for fuzzy support vector machines. In 2010 Third International Conference on Communications and Electronics (ICCE) (pp. 153-157). USA: IEEE. https://doi.org/10.1109/ICCE.2010.5670701
    Tran, Dat ; Ma, Wanli ; Sharma, Dharmendra. / A new fuzzy membership computation method for fuzzy support vector machines. 2010 Third International Conference on Communications and Electronics (ICCE). USA : IEEE, 2010. pp. 153-157
    @inproceedings{f8ab38f1b5fa4e45801f440c8b53bc17,
    title = "A new fuzzy membership computation method for fuzzy support vector machines",
    abstract = "Support vector machine (SVM) considers all data points with the same importance in classification problems, therefore SVM is very sensitive to noisy data or outliers. Current fuzzy approach to two-class SVM introduces a fuzzy membership to each data point in order to reduce the sensitivity of less important data, however computing fuzzy memberships is still a challenge. It has been found that the performance of fuzzy SVM highly depends on the computation of fuzzy memberships, hence in this paper, we propose a new method to compute fuzzy memberships and we also extend the fuzzy approach for two-class SVM to one-class SVM. Experiments performed on a number of popular data sets to evaluation the proposed fuzzy SVMs show promising classification results.",
    author = "Dat Tran and Wanli Ma and Dharmendra Sharma",
    year = "2010",
    doi = "10.1109/ICCE.2010.5670701",
    language = "English",
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    Tran, D, Ma, W & Sharma, D 2010, A new fuzzy membership computation method for fuzzy support vector machines. in 2010 Third International Conference on Communications and Electronics (ICCE). IEEE, USA, pp. 153-157, ICCE 2010: The Third International Conference on Communications and Electronics, Nha Trang, Viet Nam, 11/08/10. https://doi.org/10.1109/ICCE.2010.5670701

    A new fuzzy membership computation method for fuzzy support vector machines. / Tran, Dat; Ma, Wanli; Sharma, Dharmendra.

    2010 Third International Conference on Communications and Electronics (ICCE). USA : IEEE, 2010. p. 153-157.

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

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    AU - Ma, Wanli

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    AB - Support vector machine (SVM) considers all data points with the same importance in classification problems, therefore SVM is very sensitive to noisy data or outliers. Current fuzzy approach to two-class SVM introduces a fuzzy membership to each data point in order to reduce the sensitivity of less important data, however computing fuzzy memberships is still a challenge. It has been found that the performance of fuzzy SVM highly depends on the computation of fuzzy memberships, hence in this paper, we propose a new method to compute fuzzy memberships and we also extend the fuzzy approach for two-class SVM to one-class SVM. Experiments performed on a number of popular data sets to evaluation the proposed fuzzy SVMs show promising classification results.

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    BT - 2010 Third International Conference on Communications and Electronics (ICCE)

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    Tran D, Ma W, Sharma D. A new fuzzy membership computation method for fuzzy support vector machines. In 2010 Third International Conference on Communications and Electronics (ICCE). USA: IEEE. 2010. p. 153-157 https://doi.org/10.1109/ICCE.2010.5670701