Structure-Odor Relationships for Pyrazines

In the first classification test, class +1 contained green aroma compounds and class —1 contained compounds with nutty or bell-pepper aroma. The best prediction statistics for each kernel type are linear, C nomial, degree 2, C = 1000, AC neural, C = 10, a = 0.5, b = 0, AC 

Finally, compounds with bell-pepper aroma were considered to be in class - -1, whereas green and nutty pyrazines formed the class —1. Three kernels (RBF, polynomial, and anova) give much better predictions than does the linear SVM classifier linear, C = 10, ACp = 0.74 polynomial, degree 2, C = 10, ACp = 0.88 RBF, C = 10, y = 0.5, ACp = 0.89 neural, C = 100, a = 2, b = 1, ACp = 0.68 and anova, C = 10, y = 0.5, d = l, ACp = 0.87. We have to notice that the number of support vectors depends on the kernel type (linear, SV = 27 RBF, SV = 43 anova, SV = 31 all for training with all compounds), so for this structure-odor model, one might prefer the SVM model with a polynomial kernel that is more compact, i.e., contains a lower number of support vectors. 

In this section, we compared the prediction capabilities of five kernels, namely linear, polynomial, Gaussian radial basis function, nemal, and anova. Several guidelines that might help the modeler obtain a predictive SVM model can be extracted from these results (1) It is important to compare the predictions of a large number of kernels and combinations of parameters (2) the linear kernel should be used as a reference to compare the results from nonlinear kernels (3) some datasets can be separated with a linear hyperplane in such instances, the use of a nonlinear kernel should be avoided and (4) when the relationships between input data and class attribution are nonlinear, RBF kernels do not necessarily give the optimum SVM classifier. 

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Ivanciuc, O. (2002e) Structure-odor relationships for pyrazines with support vector machines. Internet Electron.]. Mol. Des., 1, 269-284. 

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