Table B.1.

Optimal parameter selection for SVM. Box constraint (C) and $\sigma$ of Gaussian kernel is optimized in this procedure, which can result in proper shape of decision planes and high enough classification accuracy without under- or over-fitting (PC = principal components from principal component analysis).

procedure Optimal parameter selection for SVM

repeat

Step 1: (under 2 reduced PC)

- Investigate overall shape of hyperplanes by varying parameters

- Select an optimal set (C, $\sigma$)

Step 2: (under raw 5 features)

- Check the classification accuracy

Step 3: (under raw 5 features)

- for trial = 1 … N do

Randomly divide train (70%) and test set (30%)

Run SVM training

Calculate accuracies for train and test set

- Compare the two accuracies of train and test set

Step 4: (under 3 reduced PC)

- Check shapes of hyperplanes

until Hyperplanes in Step 4 are reasonable AND

Accuracies in Step 3 are comparable