Asked a similar question the other day without an answer Link. I think maybe the question there is too big. Here I want to ask a specific one:
- 2 Class labels (Binary classification labelled with either 1 or 0);
- 50 Cases (20 labelled as 1 and 30 labelled as 0);
- 18 Features extracted (some physical quantities);
- 12 Features remained after feature selection;
How many features can be used for classification to avoid overfitting? Or there is no limit? Some paper mentioned that the number of features should be less than the 1/3 of smallest labelled data. For my example, it should less than (1/3)*20 that is around 7 that means at most 6 or 7 features can be used. Am I right? Thanks a lot.