Why area under curve (AUC) in ROC decreases when I use more variables?

Question

My dataset has 400 observations of 100 features. Each of the 400 observations belongs to 1 out of 2 classes.

I am training a neural network (patternet(15)) in MATLAB for classifying this dataset.
I don't use all the features at once, but first I use one feature (400x1), then I add a second one (400x2) and so on. At every step I calculate the AUC using perfcurve.

Here is my problem:

The AUC is changing but it is not always getting larger with each additional input.
Shouldn't the AUC be increasing as I use more features to train the network? (I always use the same divisions through divideind).

All comments and help are appreciated! Thanks!

Answer 1

Maybe the first features are more discriminatives, and the last ones only add noise ! You could try to run a random forest and see which features are the most discriminatives between the categories