MATLAB Implementation of Nearest Neighbor Algorithms with Code Enhancements

This article presents our custom implementation of nearest neighbor algorithms in MATLAB, featuring k-Nearest Neighbor (k-NN), two-way editing, repeated editing, and condensation algorithms. While basic algorithm principles are provided in the documentation, we further elaborate on their working mechanisms with specific code implementation details. The k-NN algorithm calculates Euclidean distances between test samples and training data, then classifies based on majority voting among k-closest neighbors. The editing algorithms employ distance-based prototype selection to reduce storage requirements, while condensation algorithms implement iterative prototype refinement using nearest-neighbor decision boundaries. For comprehensive theoretical foundation, we recommend referencing Bian Zhao's "Pattern Recognition". Important note: The current classification boundary plotting algorithm contains some implementation issues regarding decision surface generation, particularly in handling multi-class boundaries and non-linear separable cases. Users should modify the plotting function by incorporating proper interpolation methods and boundary smoothing techniques for better visualization results.