MATLAB Implementation of KNN Algorithm

This article presents a comprehensive implementation of the KNN (k-Nearest Neighbors) algorithm using MATLAB. As a supervised learning algorithm, KNN is widely applied to both classification and regression problems. In classification tasks, the algorithm employs distance metrics to identify k nearest neighbors for each test sample, then makes predictions based on the majority vote of these neighbors' labels. For regression problems, it calculates the average value of the k nearest neighbors' numerical outputs after determining proximity through distance measures.

The implementation includes key MATLAB functions for distance computation (such as Euclidean distance using pdist2 function), neighbor sorting, and prediction mechanisms. The code demonstrates how to handle different distance metrics (Euclidean, Manhattan, Minkowski) and voting strategies (uniform or distance-weighted). Practical considerations like feature normalization, optimal k-value selection through cross-validation, and computational efficiency improvements using vectorization are discussed. The article provides working MATLAB code examples showing data preprocessing, model training, and prediction phases, along with performance evaluation techniques using confusion matrices for classification and RMSE for regression tasks.