K-Nearest Neighbor Algorithm MATLAB Implementation with Source Code

This article presents a comprehensive discussion of the K-Nearest Neighbor algorithm along with MATLAB source code and test data. The algorithm handles both multi-class and binary classification problems effectively. Our implementation utilizes the standard Iris dataset for testing, with results showing outstanding classification accuracy of up to 98%. The K-Nearest Neighbor (KNN) classification algorithm represents a theoretically mature approach and remains one of the simplest machine learning algorithms. The core principle operates as follows: if the majority among the k most similar samples (nearest neighbors in feature space) belong to a particular class, the target sample is classified into that same category. The MATLAB code implements key components including Euclidean distance calculation using vectorized operations, efficient neighbor sorting via built-in sort functions, and majority class determination through mode identification. Furthermore, KNN algorithm finds extensive applications in various domains such as image classification and recommendation systems. This implementation provides a robust foundation for handling diverse classification tasks with excellent performance.