Journal
NEUROCOMPUTING
Volume 99, Issue -, Pages 134-143Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2012.06.012
Keywords
Machine learning; Support vector machines; Nonparallel hyperplanes; Kernel density estimation; Intra-class graph
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Funding
- Shanghai Municipal Education Commission [11YZ81]
- foundation of SHNU [SK201204]
- Shanghai Leading Academic Discipline Project [S30405]
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In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The experimental results on toy as well as publicly available datasets indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.
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