Online citations, reference lists, and bibliographies.

A Second-order Cone Programming Formulation For Twin Support Vector Machines

Sebastián Maldonado, Julio López, Miguel Carrasco
Published 2016 · Computer Science
Cite This
Download PDF
Analyze on Scholarcy
Share
Second-order cone programming (SOCP) formulations have received increasing attention as robust optimization schemes for Support Vector Machine (SVM) classification. These formulations study the worst-case setting for class-conditional densities, leading to potentially more effective classifiers in terms of performance compared to the standard SVM formulation. In this work we propose an SOCP extension for Twin SVM, a recently developed classification approach that constructs two nonparallel classifiers. The linear and kernel-based SOCP formulations for Twin SVM are derived, while the duality analysis provides interesting geometrical properties of the proposed method. Experiments on benchmark datasets demonstrate the virtues of our approach in terms of classification performance compared to alternative SVM methods.
This paper references
10.1016/S0024-3795(98)10032-0
Applications of second-order cone programming
Miguel Sousa Lobo (1998)
Julio López received his B
10.1016/j.ins.2013.11.003
Nonparallel hyperplane support vector machine for binary classification problems
Yuan-Hai Shao (2014)
Improvements on Twin Support Vector Machines
Christopher M. Bishop (2011)
10.1007/s10107-003-0425-3
Robust convex quadratically constrained programs
Donald Goldfarb (2003)
10.1007/s10107-002-0339-5
Second-order cone programming
Farid Alizadeh (2003)
Using SEDUMI 1.02, A MATLAB toolbox for optimization over symmetric cones
Judy M. Strum (1999)
10.1109/TPAMI.2007.1068
Twin Support Vector Machines for Pattern Classification
Jayadeva (2007)
10.1162/neco.2007.19.1.258
Second-Order Cone Programming Formulations for Robust Multiclass Classification
Ping Zhong (2007)
10.3233/IDA-130598
Extending twin support vector machine classifier for multi-category classification problems
Juanying Xie (2013)
10.1016/j.patcog.2012.06.019
Robust twin support vector machine for pattern classification
Zhiquan Qi (2013)
10.1007/s10489-005-4609-9
An Efficient Support Vector Machine Learning Method with Second-Order Cone Programming for Large-Scale Problems
Rameswar Debnath (2005)
10.1007/s10489-009-0176-9
The incremental learning algorithm with support vector machine based on hyperplane-distance
Cunhe Li (2009)
10.1016/S0169-7439(99)00047-7
The Mahalanobis distance
R. De Maesschalck (2000)
10.1098/rspa.1909.0075
Functions of Positive and Negative Type, and Their Connection with the Theory of Integral Equations
James Mercer (1909)
10.1007/s10489-015-0712-8
A novel multi-class SVM model using second-order cone constraints
Julio López (2015)
10.1080/10556780903483356
Interior proximal algorithm with variable metric for second-order cone programming: applications to structural optimization and support vector machines
Felipe Alvarez (2010)
Beyond accuracy , fscore and roc : A family of discriminant measures for performance eval
M Sokolova (2006)
10.1137/1.9781611972771.4
Maximum Margin Classifiers with Specified False Positive and False Negative Error Rates
SakethaNath Jagarlapudi (2007)
10.1016/j.ins.2014.07.015
Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines
Sebastián Maldonado (2014)
10.1145/1961189.1961199
LIBSVM: A library for support vector machines
Chih-Chung Chang (2011)
Learning with Kernels Improvements on twin support vector machines
B Schölkopf (2002)
10.1016/j.knosys.2014.01.025
A novel feature selection method for twin support vector machine
Lan Bai (2014)
Robust convex quadratically constrained programs. Math Program
D Goldfarb (2003)
10.1007/11941439_114
Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation
Marina Sokolova (2006)
10.1109/TSMCB.2005.850151
Supervised nonlinear dimensionality reduction for visualization and classification
Xin Geng (2005)
10.1016/j.ins.2011.05.004
Building sparse twin support vector machine classifiers in primal space
Xinjun Peng (2011)
UCI machine learning repository
Seth Hettich (1998)
10.1016/j.ins.2014.01.041
Alternative second-order cone programming formulations for support vector classification
Sebastián Maldonado (2014)
10.1080/03081079.2010.504340
Support vector machine classification with noisy data: a second order cone programming approach
Theodore B. Trafalis (2010)
Duality and Geometry in SVM Classifiers
Kristin P. Bennett (2000)
Nonlinear Programming. Classics in Applied Mathematics, Society for Industrial and Applied Mathematics
OL Mangasarian (1994)
10.7551/mitpress/4175.001.0001
Learning with kernels
Alexander J. Smola (1998)
10.1023/A:1022627411411
Support-Vector Networks
Corinna Cortes (2004)
10.1016/j.patcog.2013.11.021
Imbalanced data classification using second-order cone programming support vector machines
Sebastián Maldonado (2014)
10.1016/j.eswa.2013.01.006
Support vector machine under uncertainty: An application for hydroacoustic classification of fish-schools in Chile
Paul Bosch (2013)
10.1162/153244303321897726
A Robust Minimax Approach to Classification
Gert R. G. Lanckriet (2002)
Matrix Analysis, 1st edn
RA Horn (1990)
10.1137/1.9781611971255
Nonlinear Programming
Olvi L. Mangasarian (1969)
10.1098/rsta.1909.0016
Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations
James Mercer (1909)



This paper is referenced by
10.1080/13682199.2017.1389806
Application research on semi-definite programming optimized support vector machines
Jingzhong Hou (2018)
10.1155/2020/9562828
Shield Tunneling Parameter Matching Model and UI Interface
Gongyu Hou (2020)
10.1007/s12613-019-1724-x
A novel approach to predict green density by high-velocity compaction based on the materials informatics method
Kai-qi Zhang (2019)
10.1007/s10489-019-01498-1
Epsilon-nonparallel support vector regression
Miguel Carrasco (2019)
10.1007/s11042-016-4087-6
Sensorineural hearing loss detection via discrete wavelet transform and principal component analysis combined with generalized eigenvalue proximal support vector machine and Tikhonov regularization
Yi Chen (2016)
10.1109/ACCESS.2018.2856806
An Extraction and Classification Algorithm for Concrete Cracks Based on Machine Vision
Sun Liang (2018)
10.1016/J.NEUCOM.2019.07.072
Robust nonparallel support vector machines via second-order cone programming
Julio López (2019)
10.1155/2018/9298017
Strip Steel Surface Defects Recognition Based on SOCP Optimized Multiple Kernel RVM
Hou Jingzhong (2018)
Weighted second-order cone programming twin support vector machine for imbalanced data classification
Saeideh Roshanfekr (2019)
10.1016/j.bspc.2017.11.014
A self-adaptive frequency selection common spatial pattern and least squares twin support vector machine for motor imagery electroencephalography recognition
Duan Li (2018)
10.1007/s00034-016-0439-8
Wavelet De-Noising and Genetic Algorithm-Based Least Squares Twin SVM for Classification of Arrhythmias
Duan Li (2017)
10.1007/s10489-017-0943-y
A robust formulation for twin multiclass support vector machine
Julio López (2017)
10.1016/j.knosys.2018.04.005
Robust twin support vector regression via second-order cone programming
Julio López (2018)
10.1016/j.knosys.2020.105703
A novel twin minimax probability machine for classification and regression
Jun Ma (2020)
10.1007/s13042-019-01044-y
A survey of robust optimization based machine learning with special reference to support vector machines
Manisha Singla (2020)
Semantic Scholar Logo Some data provided by SemanticScholar