CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

In the case of FTIR, linear classifiers (PLS-DA and linear SVMs) demonstrate higher
overall predictions ( ) compared to nonlinear SVMs; in this instance, the overall
accuracy of both PLS-DA and linear SVMs is equal to 59%, whereas the accuracy of
RBF SVMs is notably lower. Since linear separation enhances the percentages of
correctly classified samples, the application of nonlinear (RBF) SVMs was found to
be unsuitable in this instance. Therefore, we can only assume that the data are
relatively easy to separate in the input space using only linear models, and hence there
is no necessity for a nonlinear projection into a high-dimensional feature space.
Indeed, according to Xu et al. (2006), the relatively complex boundaries and
formulation of kernel-based SVMs may not appeal very much in cases where the
classes are nearly or completely linearly separable. Furthermore, according to
Belousov et al. (2002), simplistic linear classification models such as PLS-DA may
frequently outperform newer, more powerful classifiers.
Finally, the e-nose dataset returns poor results for every type of classifier. Based on
the PCA scores plot of Figure 2-5, one can only assume that the widely scattered
and overlapping e-nose data may request extremely complex boundaries to
successfully discriminate the different classes; therefore, nonlinear SVMs is expected
to outperform the remaining classifiers. Indeed, it is interesting to note that the
ensemble of RBF SVMs performs significantly better than the ensembles of linear
classifiers, obtaining an accuracy of 47%. In this case, the ensemble of PLS-DA
demonstrated the lowest overall accuracy across all implemented models.
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