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    Learning classifiers without negative examples: a reduction approach

    Zhang, Dell and Lee, W.S. (2008) Learning classifiers without negative examples: a reduction approach. In: Pichappan, P. and Abraham, A. (eds.) 2008 Third International Conference on Digital Information Management. Piscataway, U.S.: IEEE, pp. 638-643. ISBN 9781424429172.

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    Abstract

    The problem of PU Learning, i.e., learning classifiers with positive and unlabelled examples (but not negative examples), is very important in information retrieval and data mining. We address this problem through a novel approach: reducing it to the problem of learning classifiers for some meaningful multivariate performance measures. In particular, we show how a powerful machine learning algorithm, support vector machine, can be adapted to solve this problem. The effectiveness and efficiency of the proposed approach have been confirmed by our experiments on three real-world datasets.

    Metadata

    Item Type: Book Section
    Additional Information: Third IEEE International Conference on Digital Information Management (ICDIM), November 13-16, 2008, London, UK, Proceedings
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Administrator
    Date Deposited: 30 May 2013 08:47
    Last Modified: 02 Dec 2016 13:26
    URI: http://eprints.bbk.ac.uk/id/eprint/7078

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