Volume 9 Number 6 (Jun. 2014)
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JSW 2014 Vol.9(6): 1494-1502 ISSN: 1796-217X
doi: 10.4304/jsw.9.6.1494-1502

Building a Biased Least Squares Support Vector Machine Classifier for Positive and Unlabeled Learning

Ting Ke, Lujia Song, Bing Yang, Xinbin Zhao, Ling Jing

Department of Applied Mathematics, College of Science, China Agricultural University, 100083, Beijing, P.R. China

Abstract—Learning from positive and unlabeled examples (PU learning) is a special case of semi-supervised binary classification. The key feature of PU learning is that there is no labeled negative training data, which makes the traditional classification techniques inapplicable. Similar to the idea of Biased-SVM which is one of the most famous classifier, a biased least squares support vector machine classifier (Biased-LSSVM) is proposed for PU learning in this paper. More specifically, we take unlabeled examples as negative examples with noise and build a least squares support vector machine classifier using two penalty parameters Cp and Cn to weight misclassification errors of positive and negative examples respectively. As we pay more attention to classify as many as positive examples correctly in PU learning, the relationship of parameters Cp and Cnis CpCn . Compared with Biased-SVM, the proposed classifier has three advantages. First, Biased-LSSVM can reflect the class labels of all examples more sufficiently and accurately than Biased-SVM. Second, Biased-LSSVM is more stable than Biased-SVM because the performance of Biased-LSSVM changes less than that of Biased-SVM over a wide ratio of positive examples in unlabeled examples. Finally, the time complexity of Biased-LSSVM is lower than that of Biased-SVM, where Biased-LSSVM only need to solve liner equations and Biased-SVM is a quadratic programming. The Experiments on two real applications, text classification and bioinformatics classification verify the above opinions and show that Biased-LSSVM is more effective than Biased-SVM and other popular methods, such as EB-SVM, ROC-SVM and S-EM.

Index Terms—positive and unlabeled learning, least squares support vector machine, text classification, bioinformatics classification

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Cite: Ting Ke, Lujia Song, Bing Yang, Xinbin Zhao, Ling Jing, "Building a Biased Least Squares Support Vector Machine Classifier for Positive and Unlabeled Learning," Journal of Software vol. 9, no. 6, pp. 1494-1502, 2014.

General Information

ISSN: 1796-217X (Online)
Frequency:  Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsweditorialoffice@gmail.com
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