Multiple birth least squares support vector machine for multi-class classification

Least squares twin support vector machine (LSTSVM) was initially designed for binary classification. However, practical problems often require the discrimination more than two categories. To tackle multi-class classification problem, a novel algorithm, called multiple birth least squares support vector machine (MBLSSVM), is proposed. Our MBLSSVM solves K quadratic programming problems (QPPs) to obtain K hyperplanes, each problem is similar to binary LSTSVM. Comparison against the Multi-LSTSVM, Multi-TWSVM, MBSVM and our MBLSSVM on both UCI datasets and ORL, YALE face datasets illustrates the effectiveness of the proposed method.

[1]  Yuan-Hai Shao,et al.  A regularization for the projection twin support vector machine , 2013, Knowl. Based Syst..

[2]  Bernhard Schölkopf,et al.  Kernel Methods in Computational Biology , 2005 .

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

[4]  Shifei Ding,et al.  An overview on twin support vector machines , 2012, Artificial Intelligence Review.

[5]  Anastasios Tefas,et al.  Minimum Class Variance Support Vector Machines , 2007, IEEE Transactions on Image Processing.

[6]  Xinjun Peng,et al.  TPMSVM: A novel twin parametric-margin support vector machine for pattern recognition , 2011, Pattern Recognit..

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

[8]  Shuai Li,et al.  A MapReduce based parallel SVM for large-scale predicting protein-protein interactions , 2014, Neurocomputing.

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

[10]  Ethem Alpaydin,et al.  Support Vector Machines for Multi-class Classification , 1999, IWANN.

[11]  Xizhao Wang,et al.  Learning from big data with uncertainty - editorial , 2015, J. Intell. Fuzzy Syst..

[12]  Jason Weston,et al.  Multi-Class Support Vector Machines , 1998 .

[13]  Yu-Lin He,et al.  Fuzzy nonlinear regression analysis using a random weight network , 2016, Inf. Sci..

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

[15]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Koby Crammer,et al.  On the Learnability and Design of Output Codes for Multiclass Problems , 2002, Machine Learning.

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

[18]  Jason Weston,et al.  Support vector machines for multi-class pattern recognition , 1999, ESANN.

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

[20]  Shifei Ding,et al.  An overview on nonparallel hyperplane support vector machine algorithms , 2013, Neural Computing and Applications.

[21]  Yuan-Hai Shao,et al.  Least squares recursive projection twin support vector machine for classification , 2012, Pattern Recognit..

[22]  Rui Guo,et al.  A Twin Multi-Class Classification Support Vector Machine , 2012, Cognitive Computation.

[23]  Shan Suthaharan,et al.  Support Vector Machine , 2016 .

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

[25]  Isabelle Guyon,et al.  Comparison of classifier methods: a case study in handwritten digit recognition , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[26]  Xizhao Wang,et al.  Fuzziness based sample categorization for classifier performance improvement , 2015, J. Intell. Fuzzy Syst..

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

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

[29]  Chunyan Miao,et al.  Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings , 2014, Neurocomputing.

[30]  Andreu Català,et al.  K-SVCR. A support vector machine for multi-class classification , 2003, Neurocomputing.

[31]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[32]  Shifei Ding,et al.  Recursive least squares projection twin support vector machines for nonlinear classification , 2014, Neurocomputing.

[33]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[34]  Yuan-Hai Shao,et al.  Multiple birth support vector machine for multi-class classification , 2012, Neural Computing and Applications.

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

[36]  Dino Isa,et al.  Text Document Preprocessing with the Bayes Formula for Classification Using the Support Vector Machine , 2008, IEEE Transactions on Knowledge and Data Engineering.

[37]  Jalal A. Nasiri,et al.  Least squares twin multi-class classification support vector machine , 2015, Pattern Recognit..