  Importance weighting is widely applicable in machine learning in general and
in techniques dealing with data covariate shift problems in particular. A
novel, direct approach to determine such importance weighting is presented. It
relies on a nearest neighbor classification scheme and is relatively
straightforward to implement. Comparative experiments on various classification
tasks demonstrate the effectiveness of our so-called nearest neighbor weighting
(NNeW) scheme. Considering its performance, our procedure can act as a simple
and effective baseline method for importance weighting.
