Nonparallel hyperplane support vector machine for PU learning

In this paper, we propose to apply the nonparallel support vector machine (NPSVM) for positive and unlabeled learning problem(PU learning problem) in which only a small positive examples and a large unlabeled examples can be used. Like Biased-SVM, NPSVM treats the unlabeled set as the negative set with noise, while NPSVM is modified so that, the first primal problem is constructed such that all the positive points make the contribution to the positive proximal hyperplane; for the second primal problem, only a part of negative points makes the contribution to the negative proximal hyperplane. And we also give the suggestion on the parameters selection. Experimental results show the efficiency of our method for PU learning problem.

[1]  Madan Gopal,et al.  Application of smoothing technique on twin support vector machines , 2008, Pattern Recognit. Lett..

[2]  C. M. Bishop,et al.  Improvements on Twin Support Vector Machines , 2011 .

[3]  Zhiquan Qi,et al.  Efficient sparse nonparallel support vector machines for classification , 2014, Neural Computing and Applications.

[4]  F. Denis.  Classification and Co-training from Positive and Unlabeled Examples , 2003 .

[5]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[6]  Yong Shi,et al.  Structural twin support vector machine for classification , 2013, Knowl. Based Syst..

[7]  Jean-Philippe Vert,et al.  A bagging SVM to learn from positive and unlabeled examples , 2010, Pattern Recognit. Lett..

[8]  Philip S. Yu,et al.  Partially Supervised Classification of Text Documents , 2002, ICML.

[9]  Yong Shi,et al.  Laplacian twin support vector machine for semi-supervised classification , 2012, Neural Networks.

[10]  Ying-jie Tian,et al.  Improved twin support vector machine , 2013, Science China Mathematics.

[11]  Wenkai Li,et al.  A Positive and Unlabeled Learning Algorithm for One-Class Classification of Remote-Sensing Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Bing Liu,et al.  Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression , 2003, ICML.

[13]  Yaonan Wang,et al.  Texture classification using the support vector machines , 2003, Pattern Recognit..

[14]  Yuan-Hai Shao,et al.  A novel margin-based twin support vector machine with unity norm hyperplanes , 2012, Neural Computing and Applications.

[15]  Pierre Geurts,et al.  Learning from positive and unlabeled examples by enforcing statistical significance , 2011, AISTATS.

[16]  Reshma Khemchandani,et al.  Twin Support Vector Machines for Pattern Classification , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Yong Shi,et al.  Recent advances on support vector machines research , 2012 .

[18]  Xiaoli Li,et al.  Learning to Classify Texts Using Positive and Unlabeled Data , 2003, IJCAI.

[19]  Thorsten Joachims,et al.  A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization , 1997, ICML.

[20]  Yong Shi,et al.  Robust twin support vector machine for pattern classification , 2013, Pattern Recognit..

[21]  Ling Zhen,et al.  A new support vector machine for the classification of positive and unlabeled examples , 2013 .

[22]  Madan Gopal,et al.  Least squares twin support vector machines for pattern classification , 2009, Expert Syst. Appl..

[23]  Kevin Chen-Chuan Chang,et al.  PEBL: positive example based learning for Web page classification using SVM , 2002, KDD.

[24]  Rémi Gilleron,et al.  Learning from positive and unlabeled examples , 2000, Theor. Comput. Sci..

[25]  Yong Shi,et al.  ν-Nonparallel support vector machine for pattern classification , 2014, Neural Computing and Applications.

[26]  Reshma Khemchandani,et al.  Optimal kernel selection in twin support vector machines , 2009, Optim. Lett..

[27]  Yong Shi,et al.  Twin support vector machine with Universum data , 2012, Neural Networks.

[28]  Philip S. Yu,et al.  Text classification without negative examples revisit , 2006, IEEE Transactions on Knowledge and Data Engineering.

[29]  Olvi L. Mangasarian,et al.  Multisurface proximal support vector machine classification via generalized eigenvalues , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Jian Yang,et al.  Recursive projection twin support vector machine via within-class variance minimization , 2011, Pattern Recognit..

[31]  Malik Yousef,et al.  One-Class SVMs for Document Classification , 2002, J. Mach. Learn. Res..

[32]  Xinjun Peng,et al.  TSVR: An efficient Twin Support Vector Machine for regression , 2010, Neural Networks.

[33]  Philip S. Yu,et al.  Building text classifiers using positive and unlabeled examples , 2003, Third IEEE International Conference on Data Mining.

[34]  Bing Liu,et al.  Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data , 2006, Data-Centric Systems and Applications.

[35]  Yuan-Hai Shao,et al.  A coordinate descent margin based-twin support vector machine for classification , 2012, Neural Networks.

[36]  Rémi Gilleron,et al.  Text Classification from Positive and Unlabeled Examples , 2002 .

[37]  Tijl De Bie,et al.  Kernel-based data fusion for gene prioritization , 2007, ISMB/ECCB.

[38]  Qiang Yang,et al.  Learning with Positive and Unlabeled Examples Using Topic-Sensitive PLSA , 2010, IEEE Transactions on Knowledge and Data Engineering.