Weighted Twin Support Vector Machines with Local Information and its application

A Twin Support Vector Machine (TWSVM), as a variant of a Multisurface Proximal Support Vector Machine via Generalized Eigenvalues (GEPSVM), attempts to improve the generalization of GEPSVM, whose solution follows from solving two quadratic programming problems (QPPs), each of which is smaller than i...

Full description

Saved in:
Bibliographic Details
Published in:Neural networks 2012-11, Vol.35, p.31-39
Main Authors: Ye, Qiaolin, Zhao, Chunxia, Gao, Shangbing, Zheng, Hao
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Computer science
control theory
systems
Computer Simulation
Data processing. List processing. Character string processing
Exact sciences and technology
GEPSVM
Memory organisation. Data processing
Pattern Recognition, Automated - methods
Similarity information
Software
Support vector
Support Vector Machine
WLTSVM
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A Twin Support Vector Machine (TWSVM), as a variant of a Multisurface Proximal Support Vector Machine via Generalized Eigenvalues (GEPSVM), attempts to improve the generalization of GEPSVM, whose solution follows from solving two quadratic programming problems (QPPs), each of which is smaller than in a standard SVM. Unfortunately, the two QPPs still lead to rather high computational costs. Moreover, although TWSVM has better classification performance than GEPSVM, a major disadvantage is it fails to exploit the underlying correlation or similarity information between any pair of data points with the same labels that may be important for classification performance as much as possible. To mitigate the above deficiencies, in this paper, we propose a novel nonparallel plane classifier, called Weighted Twin Support Vector Machines with Local Information (WLTSVM). WLTSVM mines as much underlying similarity information within samples as possible. This method not only retains the superior characteristics of TWSVM, but also has its additional advantages: (1) comparable or better classification accuracy compared to SVM, GEPSVM and TWSVM; (2) taking motivation from standard SVM, the concept of support vectors is retained; (3) more efficient than TWSVM in terms of computational costs; and (4) only one penalty parameter is considered as opposed to two in TWSVM. Finally, experiments on both simulated and real problems confirm the effectiveness of our method.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2012.06.010