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Table 4 Performance of mGOASVM using different kernels with different parameters based on leave-one-out cross validation (LOOCV) using the virus dataset

From: mGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines

Kernel Parameter Locative accuracy Actual accuracy
Linear SVM – 244/252 = 96.8% 184/207 = 88.9%
RBF SVM σ=2−2 182/252 = 72.2% 53/207 = 25.6%
RBF SVM σ=2−1 118/252 = 46.8% 87/207 = 42.0%
RBF SVM σ=1 148/252 = 58.7% 116/207 = 56.0%
RBF SVM σ=21 189/252 = 75.0% 142/207 = 68.6%
RBF SVM σ=22 223/252 = 88.5% 154/207 = 74.4%
RBF SVM σ=23 231/252 = 91.7% 150/207 = 72.5%
RBF SVM σ=24 233/252 = 92.5% 115/207 = 55.6%
RBF SVM σ=25 136/252 = 54.0% 5/207 = 2.4%
Polynomial SVM d=2 231/252 = 91.7% 180/207 = 87.0%
Polynomial SVM d=3 230/252 = 91.3% 178/207 = 86.0%
  1. The penalty parameter (C) was set to 0.1 for all cases. σ is the kernel parameter for the RBF SVM; d is the polynomial degree in the Polynomial SVM.
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BMC Bioinformatics

ISSN: 1471-2105

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