Nearest Neighbor-based Importance Weighting
This work provides a new, straightforward baseline method for importance weighting, which is beneficial for machine learning practitioners dealing with covariate shift problems.
This paper introduces a new method for importance weighting using a nearest neighbor classification scheme. The proposed Nearest Neighbor Weighting (NNeW) scheme is shown to be effective across various classification tasks, positioning it as a simple and effective baseline.
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.