A novel pattern recognition algorithm: Combining ART network with SVM to reconstruct a multi-class classifier

Author(s): Li, H. | Liu, J.F. | Wang, A. | Yu, Z.G. | Yuan, W.J. |

Year: 2009

Citation: COMPUTERS & MATHEMATICS WITH APPLICATIONS Volume: 57 Issue: 11-12 Pages: 1908-1914

Abstract: Based on the principle of one-against-one support vector machines (SVMs) multi-class classification algorithm, this paper proposes an extended SVMs method which couples adaptive resonance theory (ART) network to reconstruct a multi-class classifier. Different coupling strategies to reconstruct a multi-class classifier from binary SVM classifiers are compared with application to fault diagnosis of transmission line. Majority voting, a mixture matrix and self-organizing map (SOM) network are compared in reconstructing the global classification decision. In order to evaluate the method's efficiency, one-against-all, decision directed acyclic graph (DDAG) and decision-tree (DT) algorithm based SVM are compared too. The comparison is done with Simulations and the best method is validated with experimental data.

Topics: Machine Learning, Models: ART 1, Modified ART, Self Organizing Maps,

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