MATLAB Source Code Implementation for KNN Classification

In this article, we will explore the MATLAB source code implementation for KNN (K-Nearest Neighbors) classification. KNN classification is a widely-used machine learning algorithm frequently applied to classification problems. The fundamental principle of this algorithm involves classifying new data points based on a known dataset with classification labels. For each new data point, the KNN algorithm calculates distances in the feature space, selects the K closest known data points, and determines the classification of the new point based on the majority labels of these K neighbors. Understanding the source code implementation of the KNN algorithm is crucial. While MATLAB provides various built-in functions for KNN classification, examining the underlying code helps deepen our comprehension of the algorithm's mechanics and enables customization according to specific requirements. This article will provide a detailed analysis of MATLAB's KNN classification source code, explaining the purpose and functionality of each code segment. Key implementation aspects we'll cover include: distance calculation methods (Euclidean, Manhattan, or custom metrics), nearest neighbor selection using sorting algorithms, majority voting mechanisms for classification decisions, and parameter optimization for K-value selection. We'll also examine how MATLAB handles edge cases like tie-breaking scenarios and distance-weighted voting variations.