I' want to train a model for classifiation. I have a pretty small data-set (about 400 data unevenly distributed among three classes). To evaluate my model, i want to perform cross-validation, with a step of feature-selection (by wrapper) for each fold. I read that using different data for feature selection and for training the model (and of course for validation) is better in order to avoid overfiting.
Do you agree, or spliting the data in two set, one for feature selection and training, and the other for validation is good enough ?
If not, what proportion of data should i use for the feature selection - training - validation set of each fold ?