Bayesian adaptive nearest neighbor

: The k nearest neighbor classification (k-NN) is a very simple and popular method for classification. However, it suffers from a major drawback, it assumes constant local class posterior probability. It is also highly dependent on and sensitive to the choice of the number of neighbors k. In addition, it severely lacks the desired probabilistic formulation. In this article, we propose a Bayesian adaptive nearest neighbor method (BANN) that can adaptively select the shape of the neighborhood and the number of neighbors k. The shape of the neighborhood is automatically selected according to the concentration of the data around each query point with the help of discriminants. The neighborhood size is not predetermined and is kept free using a prior distribution. Thus, we are able to make the model to select the appropriate neighborhood size. The model is fitted using Markov Chain Monte Carlo (MCMC), so we are not using exactly one neighborhood size but a mixture of k. Our BANN model is hi...

• Type: Journal
• Year: 2010
• Where: SADM
• Authors: Ruixin Guo, Sounak Chakraborty
• Keywords: Adaptive Nearest Neighbor, Applied Computing, Nearest Neighbor, Neighborhood Size, SADM 2010