# Cross-Validation in binary classification using only 10 positive samples (SVM)

I have a binary classification problem for which only $10$ positive samples are available for training. Negatives are in general in abundance, but I choose to use solely $70$ ($7$ negatives per one positive). I am trying to learn a kernel SVM (using the RBF kernel), thus I want to optimize the pair of parameters $C$, $\gamma$. I conduct grid search and I am wondering which division of the training set is more appropriate. Should I use $3$-, $5$-, or $10$-fold cross-validation? Something else maybe?

I am particularly interested in the case of $\mathbf{10}$-fold cross-validation, because I have only $10$ positive samples. Would that be a good approach?

• With only 10 positives, I think it would be better to use repeated 5 fold cross-validation. Something like 10 times iterated 5 fold cv. – Marc Claesen Apr 18 '15 at 15:17
• Thanks @MarcClaesen, the problem is that I cannot run so many iterations, because of complexity reasons (it's not a standard SVM), but would you think that a standard $5$-fold cross-validation procedure would be reasonable? – nullgeppetto Apr 18 '15 at 15:20