Improving the Predictive Performances of $k$ Nearest Neighbors Learning by Efficient Variable Selection
This work addresses the challenge of enhancing accuracy in k nearest neighbors for data analysis, but it appears incremental as it builds on existing variable selection techniques.
The paper tackles the problem of improving predictive performance in k nearest neighbors learning by using an efficient forward selection of predictor variables, resulting in a sharp improvement that outperforms regression models under stepwise selection on simulated and real-world data.
This paper computationally demonstrates a sharp improvement in predictive performance for $k$ nearest neighbors thanks to an efficient forward selection of the predictor variables. We show both simulated and real-world data that this novel repeatedly approaches outperformance regression models under stepwise selection