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.
Bibliographical noteFunding Information:
The first author was supported by FONDECYT project 1130905 , the second one was funded by FONDECYT project 1160894 , and third author was supported by FONDECYT project 1140831 . The work reported in this paper has been partially funded by Millennium Scientific Institute on Complex Engineering Systems Institute (ICM: P-05-004-F, CONICYT: FB016).
© 2016 Elsevier Ltd. All rights reserved.
- Nonparallel hyperplane SVM
- Second-order cone programming
- Support vector classification