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. Author manuscript; available in PMC: 2015 Jan 9.
Published in final edited form as: Conf Proc IEEE Eng Med Biol Soc. 2014 Aug;2014:5796–5799. doi: 10.1109/EMBC.2014.6944945

Figure 4.

Figure 4

In-sample optimization with F-score via 5-fold cross-validation with a training dataset. A range of costs (C’s, penalties) and γ’s in the RBF kernel (proportional to a default value in LIBSVM) defines a grid where the optimal parameter pair, i.e. the pair that maximizes the F-score, is chosen. In this example, the pair of C = 21 and γ = 22 was selected (maximum F-score = 0.93). The SVM is trained using the optimal pair of parameters with the training dataset, and then evaluated on an untouched test dataset.