A second-order cone programming formulation for nonparallel hyperplane support vector machine

Miguel Carrasco, Julio López, Sebastián Maldonado

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

All rights reserved. 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.
Original languageAmerican English
Pages (from-to)95-104
Number of pages10
JournalExpert Systems with Applications
Volume54
DOIs
StatePublished - 15 Jul 2016

Keywords

  • Nonparallel hyperplane SVM
  • Second-order cone programming
  • Support vector classification

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