Weighted linear loss twin support vector machine for large-scale classification

In this paper, we formulate a twin-type support vector machine for large-scale classification problems, called weighted linear loss twin support vector machine (WLTSVM). By introducing the weighted linear loss, our WLTSVM only needs to solve simple linear equations with lower computational cost, and meanwhile, maintains the generalization ability. So, it is able to deal with large-scale problems efficiently without any extra external optimizers. The experimental results on several benchmark datasets indicate that, comparing to TWSVM, our WLTSVM has comparable classification accuracy but with less computational time.

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

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

[3]  Glenn Fung,et al.  Proximal support vector machine classifiers , 2001, KDD '01.

[4]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[5]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

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

[7]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[8]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

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

[10]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[11]  Johan A. K. Suykens,et al.  Weighted least squares support vector machines: robustness and sparse approximation , 2002, Neurocomputing.

[12]  Yousef Saad,et al.  Iterative methods for sparse linear systems , 2003 .

[13]  Yong Shi,et al.  Successive Overrelaxation for Laplacian Support Vector Machine , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[14]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[15]  Yingjie Tian,et al.  Large-scale linear nonparallel support vector machine solver , 2014, Neurocomputing.

[16]  Yuan-Hai Shao,et al.  An efficient support vector machine approach for identifying protein S-nitrosylation sites. , 2011, Protein and peptide letters.

[17]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[18]  Yuan-Hai Shao,et al.  Weighted Linear Loss Support Vector Machine for Large Scale Problems , 2014, ITQM.

[19]  Johan A. K. Suykens,et al.  Non-parallel support vector classifiers with different loss functions , 2014, Neurocomputing.

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

[21]  Nai-Yang Deng,et al.  Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions , 2012 .

[22]  Yuan-Hai Shao,et al.  Proximal parametric-margin support vector classifier and its applications , 2012, Neural Computing and Applications.

[23]  Yuan-Hai Shao,et al.  An efficient weighted Lagrangian twin support vector machine for imbalanced data classification , 2014, Pattern Recognit..

[24]  Yuan-Hai Shao,et al.  Nonparallel hyperplane support vector machine for binary classification problems , 2014, Inf. Sci..

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

[26]  Bernhard Schölkopf,et al.  Support Vector Machine Applications in Computational Biology , 2004 .

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

[28]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[29]  Dong Xu,et al.  A twin-hypersphere support vector machine classifier and the fast learning algorithm , 2013, Inf. Sci..

[30]  Thorsten Joachims,et al.  Making large-scale support vector machine learning practical , 1999 .

[31]  Sam Kwong,et al.  A vector-valued support vector machine model for multiclass problem , 2013, Inf. Sci..

[32]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[33]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[34]  David G. Stork,et al.  Pattern Classification , 1973 .

[35]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

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

[37]  Yue-Shi Lee,et al.  A support vector machine-based context-ranking model for question answering , 2013, Inf. Sci..

[38]  Yuan-Hai Shao,et al.  Improvements on Twin Support Vector Machines , 2011, IEEE Transactions on Neural Networks.

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

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

[41]  Yuh-Jye Lee,et al.  RSVM: Reduced Support Vector Machines , 2001, SDM.

[42]  Johan A. K. Suykens,et al.  Least squares support vector machine classifiers: a large scale algorithm , 1999 .