Labeling one class for cross validation in libsvm matlab

Question

I want to use one-class classification using LibSVM in MATLAB.

I want to train data and use cross validation, but I don\'t know what I have to do to label the outlie

Answer 1

According to http://www.joint-research.org/wp-content/uploads/2011/07/lukashevich2009Using-One-class-SVM-Outliers-Detection.pdf "Due to the lack of class labels in the one-class SVM, it is not possible to optimize the kernel parameters using cross-validation". However, according to the LIBSVM FAQ that is not quite correct:

Q: How do I choose parameters for one-class SVM as training data are in only one class? You have pre-specified true positive rate in mind and then search for parameters which achieve similar cross-validation accuracy.

Furthermore the README for the libsvm source says of the input data: "For classification, label is an integer indicating the class label ... For one-class SVM, it's not used so can be any number."

I think your outliers should not be included in the training data - libsvm will ignore the training labels. What you are trying to do is find a hypersphere that contains good data but not outliers. If you train with outliers in the data LIBSVM will try yo find a hypersphere that includes the outliers, which is exactly what you don't want. So you will need a training dataset without outliers, a validation dataset with outliers for choosing parameters, and a final test dataset to see whether your model generalizes.