NEUROSVM: An Architecture to Reduce the Effect of the Choice of Kernel on the Performance of SVM
Pradip Ghanty, Samrat Paul, Nikhil R. Pal; 10(21):591−622, 2009.
In this paper we propose a new multilayer classifier architecture. The proposed hybrid architecture has two cascaded modules: feature extraction module and classification module. In the feature extraction module we use the multilayered perceptron (MLP) neural networks, although other tools such as radial basis function (RBF) networks can be used. In the classification module we use support vector machines (SVMs)---here also other tool such as MLP or RBF can be used. The feature extraction module has several sub-modules each of which is expected to extract features capturing the discriminating characteristics of different areas of the input space. The classification module classifies the data based on the extracted features. The resultant architecture with MLP in feature extraction module and SVM in classification module is called NEUROSVM. The NEUROSVM is tested on twelve benchmark data sets and the performance of the NEUROSVM is found to be better than both MLP and SVM. We also compare the performance of proposed architecture with that of two ensemble methods: majority voting and averaging. Here also the NEUROSVM is found to perform better than these two ensemble methods. Further we explore the use of MLP and RBF in the classification module of the proposed architecture. The most attractive feature of NEUROSVM is that it practically eliminates the severe dependency of SVM on the choice of kernel. This has been verified with respect to both linear and non-linear kernels. We have also demonstrated that for the feature extraction module, the full training of MLPs is not needed.
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