Multiple Distribution Data Description Learning Method for Novelty Detection

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

    7 Citations (Scopus)

    Abstract

    Current data description learning methods for novelty detection such as support vector data description and small sphere with large margin construct a spherically shaped boundary around a normal data set to separate this set from abnormal data. The volume of this sphere is minimized to reduce the chance of accepting abnormal data. However those learning methods do not guarantee that the single spherically shaped boundary can best describe the normal data set if there exist some distinctive data distributions in this set. We propose in this paper a new data description learning method that constructs a set of spherically shaped boundaries to provide a better data description to the normal data set. An optimisation problem is proposed and solving this problem results in an iterative learning algorithm to determine the set of spherically shaped boundaries. We prove that the classification error will be reduced after each iteration in our learning method. Experimental results on 23 well-known data sets show that the proposed method provides lower classification error rates
    Original languageEnglish
    Title of host publicationThe 2011 International Joint Conference on Neural Networks (IJCNN 2011)
    EditorsAli A Minai
    Place of PublicationSan Jose, USA
    PublisherIEEE
    Pages2321-2326
    Number of pages6
    Volume1
    ISBN (Electronic)9781424496372
    ISBN (Print)9781424496358
    DOIs
    Publication statusPublished - 31 Jul 2011
    EventThe International Joint Conference on Neural Networks - San Jose, San Jose, United States
    Duration: 31 Jul 20115 Aug 2011

    Conference

    ConferenceThe International Joint Conference on Neural Networks
    Abbreviated titleIJCNN
    CountryUnited States
    CitySan Jose
    Period31/07/115/08/11

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    Data description
    Learning algorithms

    Cite this

    Tran, D., Ma, W., & Sharma, D. (2011). Multiple Distribution Data Description Learning Method for Novelty Detection. In A. A. Minai (Ed.), The 2011 International Joint Conference on Neural Networks (IJCNN 2011) (Vol. 1, pp. 2321-2326). San Jose, USA: IEEE. https://doi.org/10.1109/IJCNN.2011.6033518
    Tran, Dat ; Ma, Wanli ; Sharma, Dharmendra. / Multiple Distribution Data Description Learning Method for Novelty Detection. The 2011 International Joint Conference on Neural Networks (IJCNN 2011). editor / Ali A Minai. Vol. 1 San Jose, USA : IEEE, 2011. pp. 2321-2326
    @inproceedings{ffc3f8aa1fb9451b840554cc6ef72d9d,
    title = "Multiple Distribution Data Description Learning Method for Novelty Detection",
    abstract = "Current data description learning methods for novelty detection such as support vector data description and small sphere with large margin construct a spherically shaped boundary around a normal data set to separate this set from abnormal data. The volume of this sphere is minimized to reduce the chance of accepting abnormal data. However those learning methods do not guarantee that the single spherically shaped boundary can best describe the normal data set if there exist some distinctive data distributions in this set. We propose in this paper a new data description learning method that constructs a set of spherically shaped boundaries to provide a better data description to the normal data set. An optimisation problem is proposed and solving this problem results in an iterative learning algorithm to determine the set of spherically shaped boundaries. We prove that the classification error will be reduced after each iteration in our learning method. Experimental results on 23 well-known data sets show that the proposed method provides lower classification error rates",
    keywords = "Machine Learning, Novelty Detection",
    author = "Dat Tran and Wanli Ma and Dharmendra Sharma",
    year = "2011",
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    Tran, D, Ma, W & Sharma, D 2011, Multiple Distribution Data Description Learning Method for Novelty Detection. in AA Minai (ed.), The 2011 International Joint Conference on Neural Networks (IJCNN 2011). vol. 1, IEEE, San Jose, USA, pp. 2321-2326, The International Joint Conference on Neural Networks, San Jose, United States, 31/07/11. https://doi.org/10.1109/IJCNN.2011.6033518

    Multiple Distribution Data Description Learning Method for Novelty Detection. / Tran, Dat; Ma, Wanli; Sharma, Dharmendra.

    The 2011 International Joint Conference on Neural Networks (IJCNN 2011). ed. / Ali A Minai. Vol. 1 San Jose, USA : IEEE, 2011. p. 2321-2326.

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

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    N2 - Current data description learning methods for novelty detection such as support vector data description and small sphere with large margin construct a spherically shaped boundary around a normal data set to separate this set from abnormal data. The volume of this sphere is minimized to reduce the chance of accepting abnormal data. However those learning methods do not guarantee that the single spherically shaped boundary can best describe the normal data set if there exist some distinctive data distributions in this set. We propose in this paper a new data description learning method that constructs a set of spherically shaped boundaries to provide a better data description to the normal data set. An optimisation problem is proposed and solving this problem results in an iterative learning algorithm to determine the set of spherically shaped boundaries. We prove that the classification error will be reduced after each iteration in our learning method. Experimental results on 23 well-known data sets show that the proposed method provides lower classification error rates

    AB - Current data description learning methods for novelty detection such as support vector data description and small sphere with large margin construct a spherically shaped boundary around a normal data set to separate this set from abnormal data. The volume of this sphere is minimized to reduce the chance of accepting abnormal data. However those learning methods do not guarantee that the single spherically shaped boundary can best describe the normal data set if there exist some distinctive data distributions in this set. We propose in this paper a new data description learning method that constructs a set of spherically shaped boundaries to provide a better data description to the normal data set. An optimisation problem is proposed and solving this problem results in an iterative learning algorithm to determine the set of spherically shaped boundaries. We prove that the classification error will be reduced after each iteration in our learning method. Experimental results on 23 well-known data sets show that the proposed method provides lower classification error rates

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    Tran D, Ma W, Sharma D. Multiple Distribution Data Description Learning Method for Novelty Detection. In Minai AA, editor, The 2011 International Joint Conference on Neural Networks (IJCNN 2011). Vol. 1. San Jose, USA: IEEE. 2011. p. 2321-2326 https://doi.org/10.1109/IJCNN.2011.6033518