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Learning with positive and unlabeled examples using biased twin support vector machine

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Abstract

PU classification problem (‘P’ stands for positive, ‘U’ stands for unlabeled), which is defined as the training set consists of a collection of positive and unlabeled examples, has become a research hot spot recently. In this paper, we design a new classification algorithm to solve the PU problem: biased twin support vector machine (B-TWSVM). In B-TWSVM, two nonparallel hyperplanes are constructed such that the positive examples can be classified correctly, and the number of unlabeled examples classified as positive is minimized. Moreover, considering that the unlabeled set also contains positive data, different penalty parameters for positive and negative data are allowed in B-TWSVM. Experimental results demonstrate that our method outperforms the state-of-the-art methods in most cases.

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Acknowledgments

This work has been partially supported by grants from Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality (PHR201107123), the Key Laboratory for Urban Geomatics of National Administration of Surveying, Mapping and Geoinformation Foundation (20111216N), Beijing Municipal Commission of Education, Science and Technology Development (KM201210016014), and Scientific Research Foundation of Beijing University of Civil Engineering and Architecture (00331609054).

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Authors and Affiliations

  1. School of Science, Beijing University of Civil Engineering and Architecture, Beijing, 102616, China

    Zhijie Xu

  2. Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, 100190, China

    Zhiquan Qi

  3. Key Laboratory for Urban Geomatics of National Administration of Surveying, Mapping and Geoinformation, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China

    Jianqin Zhang

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  1. Zhijie Xu
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  2. Zhiquan Qi
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  3. Jianqin Zhang
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Correspondence to Zhijie Xu.

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Xu, Z., Qi, Z. & Zhang, J. Learning with positive and unlabeled examples using biased twin support vector machine. Neural Comput & Applic 25, 1303–1311 (2014). https://doi.org/10.1007/s00521-014-1611-3

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  • DOI: https://doi.org/10.1007/s00521-014-1611-3

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