## Comparison of a distance-based likelihood ratio test and k-nearest neighbor classification methods

Several studies of the k-nearest neighbor (KNN) classifier have proposed the use of non-uniform weighting on the k neighbors. It has been suggested that the distance to each neighbor can be used to calculate the individual weights in a weighted KNN approach; however, a consensus has not yet been reached on the best method or framework for calculating weights using the distances. In this paper, a distance likelihood ratio test will be discussed and evaluated using simulated data. The distance likelihood ratio test (DLRT) shares several characteristics with the distance-weighted k-nearest neighbor methods but approaches the use of distance from a different perspective. Results illustrate the ability of the distance likelihood ratio test to approximate the likelihood ratio and compare the DLRT to two other k-neighborhood classification rules that utilize distance-weighting. The DLRT performs favorably in comparisons of the classification performance using the simulated data and provides an alternative nonparametric classification method for consideration when designing a distance-weighted KNN classification rule. ©2008 IEEE.

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*Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008*(pp. 362–367). https://doi.org/10.1109/MLSP.2008.4685507

*Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008*, 362–67, 2008. https://doi.org/10.1109/MLSP.2008.4685507.

*Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008*, 2008, pp. 362–67.

*Scopus*, doi:10.1109/MLSP.2008.4685507.