I have 12 positive training sets (cancer cells treated with drugs with each of 12 different mechanisms of action). For each of these positive training sets, I would like to train a support-vector machine to distinguish it from a negative set of equal size sampled from the experiment. Each set has between 1000 and 6000 cells, and there are 476 features (image features) of each cell, each scaled linearly to [0, 1].
I use LIBSVM and the Gaussian RGB kernel. Using five-fold crossvalidation, I have done a grid search for log₂ C ∈ [-5, 15] and log₂ ɣ ∈ [-15, 3]. The results are as follows:
I was disappointed that there is not a single set of parameters that give high accuracies for all 12 classification problems. I was also surprised that the grids do not generally show a high-accuracy region surrounded by lower accuracies. Does this just mean that I need to expand the search parameter space, or is the grid search an indication that something else is wrong?