I am working on a binary class classification problem. Each sample is a vector 1x101, I have a lot of data samples more than 150k I tried training a linear svm and a non-linear svm (RBF) "zscore normalization is used in both cases". surprisingly, the linear does better than the svm (RBF). I am trying to explain this by considering the following points:
- I beleive that the quality of my feature is not very good.
- I think the nonlinear case experinces a kind of overfitting.
my question is how to explain this behaviour?!! does what I am thinking in make sense?!! I am thinking in using Adaboost to perform the training, is it a good idea or not?