Paper Info
Title
A second-order cone programming formulation for nonparallel hyperplane support vector machine.
Abstract
Novel robust SVM approach based on second-order cone programming.An extension for the method Nonparallel Hyperplane SVM is proposed.A geometrically grounded method based on the concept of ellipsoids.Superior classification performance is achieved in experiments on benchmark datasets. Expert systems often rely heavily on the performance of binary classification methods. The need for accurate predictions in artificial intelligence has led to a plethora of novel approaches that aim at correctly predicting new instances based on nonlinear classifiers. In this context, Support Vector Machine (SVM) formulations via two nonparallel hyperplanes have received increasing attention due to their superior performance. In this work, we propose a novel formulation for the method, Nonparallel Hyperplane SVM. Its main contribution is the use of robust optimization techniques in order to construct nonlinear models with superior performance and appealing geometrical properties. Experiments on benchmark datasets demonstrate the virtues in terms of predictive performance compared with various other SVM formulations. Managerial insights and the relevance for intelligent systems are discussed based on the experimental outcomes.
Year
DOI
Venue
2016
10.1016/j.eswa.2016.01.044
Expert Syst. Appl.
Keywords
Field
DocType
Support vector classification,Nonparallel hyperplane SVM,Second-order cone programming
Second-order cone programming,Nonlinear system,Binary classification,Pattern recognition,Intelligent decision support system,Robust optimization,Computer science,Support vector machine,Expert system,Artificial intelligence,Hyperplane,Machine learning
Journal
Volume
Issue
ISSN
54
C
0957-4174
Citations 
PageRank 
References 
4
0.43
25
Authors
3
Name
Order
Citations
PageRank
Miguel Carrasco1214.35
Julio López212413.49
Sebastián Maldonado350832.45