Neural Computing and Applications

, Volume 25, Issue 6, pp 1303–1311 | Cite as

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

Original Article
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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.

Keywords

Machine learning Classification Support vector machine 
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Notes

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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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.School of ScienceBeijing University of Civil Engineering and ArchitectureBeijingChina
  2. 2.Research Center on Fictitious Economy and Data ScienceChinese Academy of SciencesBeijingChina
  3. 3.Key Laboratory for Urban Geomatics of National Administration of Surveying, Mapping and GeoinformationBeijing University of Civil Engineering and ArchitectureBeijingChina

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