I am working on classification issue. My training set contains of 10D features vectors. As a training model I am going to use Fisher or Neural Network. Here is a plot of the correlation matrix for a input variables for both signal and background.
My very first guess was to reject highly correlated variables. That why I removed seed_ty, seed_nLayers, seed_p.
Is it a good choice? Do you know any better way to choose the most optimal set of input parameters. How the answer for such question depend on what model do we use? I can use greedy feature selection method (looping over all possible feature subset) but It is not the smartest idea.
PS 1. The below plots presents the distribution of the input variables.
PS 2. Do you know any good book about feature selection?