# Correlation based feature selection with cross-validation

I am trying to do a correlation based feature selection for a classification model. Dataset details is given below.

Training :- 38 Samples, 7130 features. represented as T

Testing :- 34 Samples, 7130 features. represented as S

Target: 2 classes (Yes | No )

So, I need to select first 100 features highly correlated with class variable.

Here I have mentioned different approaches that I've tried, but I am not sure which approach is best. Please go through approaches given below and comment the best one.

1) Combined T and S to single table X = T + S. Let {A} be set of all features and a is an element of {A}. I calculated correlation of all a and then selected top 100 features to create a new dataset with dimension 72x100

2) I applied correlation selection on T. The selected features will be extracted from S. We get new datasets T and S

• This is not correlation based, but. Another approach might be to use lasso. Regularized regression is meant to handle the $'n<p'$ issue. Lasso will in fact set some (most?) of the regression coefficients to zero. The glmnet package will do this. – meh May 31 '17 at 13:49