Optimized Nearest Neighbor Methods Cam Weighted Distance & Statistical Confidence

Nearest neighbor classification methods are a useful and a relatively straightforward to implement classification technique. However, despite such appeal, they still suffer from the curse of dimensionality. Additionally, the nature of the data sets may not be wholly applicable to the model assumed in the nearest neighbor methods. As such there have been many proposed optimizations. Two such optimizations studied in this project are statistical confidence and “cam-weighted” distance measure. For statistical confidence, a confidence measure is used to adapt the k value for k nearest neighbor to allow a more optimal set of neighbors for classification. Cam-weighted distance mimics attractive and repulsive forces of prototypes in determining its non-metric distance measure. This report documents the progress of studying these methods and their interaction.