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Neural Computing and Applications

Erschienen in:

01.11.2014 | Original Article

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

verfasst von: Zhijie Xu, Zhiquan Qi, Jianqin Zhang

Erschienen in: Neural Computing and Applications | Ausgabe 6/2014

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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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Liu B, Lee WS, Yu PS, Li X (2002) Partially supervised classification of text documents. In: Machine learning-international workshop then conference, Citeseer, pp 387–394

Yu H, Han J, Chang KC-C (2002) Pebl: positive example based learning for web page classification using svm. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 239–248

Li X, Liu B (2003) Learning to classify texts using positive and unlabeled data. In: International joint conference on artificial intelligence, vol 18. Lawrence Erlbaum Associates Ltd, pp 587–594

Rocchio Jr J (1971) Relevance feedback in information retrieval. In: The SMART system experiments in automatic document processing. Prentice Hall, New York, pp 313–323

Liu B, Dai Y, Li X, Lee WS, Yu PS (2003) Building text classifiers using positive and unlabeled examples. In: Data mining, 2003. ICDM 2003. Third IEEE international conference on, IEEE, pp 179–186

Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc Ser B (Methodological) 39(1):1–38

Mangasarian OL, Wild EW (2006) Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Trans Pattern Anal Mach Intell 28(1):69–74CrossRef

Khemchandani R, Chandra S et al (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905–910CrossRef

Kumar MA, Gopal M (2008) Application of smoothing technique on twin support vector machines. Pattern Recogn Lett 29(13):1842–1848CrossRef

10.

Khemchandani R, Chandra S et al (2009) Optimal kernel selection in twin support vector machines. Optim Lett 3(1):77–88MathSciNetCrossRefMATH

11.

Arun Kumar M, Gopal M (2009) Least squares twin support vector machines for pattern classification. Expert Syst Appl 36(4):7535–7543CrossRef

12.

Ghorai S, Mukherjee A, Dutta PK (2009) Nonparallel plane proximal classifier. Signal Process 89(4):510–522CrossRefMATH

13.

Peng X (2010) Primal twin support vector regression and its sparse approximation. Neurocomputing 73(16):2846–2858CrossRef

14.

Peng X (2012) Efficient twin parametric insensitive support vector regression model. Neurocomputing 79:26–38CrossRef

15.

Shao Y-H, Deng N-Y (2012) A coordinate descent margin based-twin support vector machine for classification. Neural Netw 25:114–121CrossRefMATH

16.

Shao Y-H, Zhang C-H, Wang X-B, Deng N-Y (2011) Improvements on twin support vector machines. IEEE Trans Neural Netw 22(6):962–968CrossRef

17.

Shao YH, Chen WJ, Deng NY (2014) Nonparallel hyperplane support vector machine for binary classification problems. Inf Sci 263:22–35

18.

Tian Y, Qi Z, Ju X, Shi Y, Liu X (2014) Nonparallel support vector machines for pattern classification. IEEE Trans Cybern. doi:10.1109/TCYB.2013.2279167

19.

Vapnik V (1999) The nature of statistical learning theory. Springer, Berlin

20.

Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167CrossRef

21.

Wang Z, Chen J, Qin M (2010) Nonparallel planes support vector machine for multi-class classification. In: 2010 international conference on logistics systems and intelligent management (ICLSIM) vol 1, pp 581–585

22.

Asuncion A, Newman DJ (2007) UCI machine learning repository. University of California, School of Information and Computer Science, Irvine, CA. http://www.ics.uci.edu/~mlearn/MLRepository.html

Titel: Learning with positive and unlabeled examples using biased twin support vector machine
verfasst von: Zhijie Xu
Zhiquan Qi
Jianqin Zhang
Publikationsdatum: 01.11.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 6/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1611-3

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