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Global and local learning from positive and unlabeled examples

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Abstract

In common binary classification scenarios, learning algorithms assume the presence of both positive and negative examples. Unfortunately, in many practical areas, only limited labeled positive examples and large amounts of unlabeled examples are available, but there are no negative examples. In such cases, the algorithm that only exploits positive and unlabeled examples is needed. Such learning is termed as positive and unlabeled (PU) learning. In this paper, a novel classifier called global and local learning classifier (GLLC) for PU learning is proposed. The advantages of GLLC are as follows: (1) both intrinsic geometric structure and accurate positive information of PU data are exploited from global learning. (2) The smoothness and manifold of data are reflected sufficiently from local learning. (3) The algorithm of GLLC has faster training speed because the linear equations are solved. (4) The experiments on both synthetic and real datasets verify the above opinions and show that the classification result of GLLC is much better than those popular methods, such as LUHC, Pulce, BSVM, NB and so on.

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Acknowledgments

This work is supported by the youth innovative Foundation of Tianjin University of Science & Technology (2016LG30, 2016LG29).

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Authors and Affiliations

  1. Department of Mathematics, College of Science, Tianjin University of Science & Technology, 300457, Tianjin, People’s Republic of China

    Ting Ke, Hui Lv, Lidong Zhang & Yaping Hu

  2. College of Science, China Agricultural University, Beijing, 100083, People’s Republic of China

    Ling Jing

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  1. Ting Ke
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  2. Ling Jing
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  3. Hui Lv
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  4. Lidong Zhang
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Correspondence to Ting Ke.

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Ke, T., Jing, L., Lv, H. et al. Global and local learning from positive and unlabeled examples. Appl Intell 48, 2373–2392 (2018). https://doi.org/10.1007/s10489-017-1076-z

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