  Relief based algorithms have often been claimed to uncover feature
interactions. However, it is still unclear whether and how interaction terms
will be differentiated from marginal effects. In this paper, we propose
IMMIGRATE algorithm by including and training weights for interaction terms.
Besides applying the large margin principle, we focus on the robustness of the
contributors of margin and consider local and global information
simultaneously. Moreover, IMMIGRATE has been shown to enjoy attractive
properties, such as robustness and combination with Boosting. We evaluate our
proposed method on several tasks, which achieves state-of-the-art results
significantly.
