Dynamic Oscillating Search algorithm for feature selection

We introduce a new feature selection method suitable for non-monotonic criteria, i.e., for wrapper-based feature selection. Inspired by oscillating search, the dynamic oscillating search: (i) is deterministic, (ii) optimizes subset size, (iii) has built-in preference of smaller subsets, (iv) has higher optimization performance than other sequential methods. We show that the new algorithm is capable of over-performing older methods not only in criterion maximization ability but in some cases also in obtaining subsets that generalize better.