K-Nearest Neighbors (KNN) Algorithm Implementation with MATLAB Code

This article introduces the concept of the K-Nearest Neighbors (KNN) algorithm and its applications in machine learning. KNN is an unsupervised learning algorithm that classifies samples by calculating distances between each sample and all training set samples. The algorithm's advantages include simplicity, ease of implementation, and suitability for most datasets. In this article, we provide example MATLAB code implementing the KNN algorithm along with a dataset for readers to practice and test. To enhance understanding, we explain each step of the code implementation in detail, including parameter selection rationale and distance calculation methods (such as Euclidean or Manhattan distance). We discuss key MATLAB functions like knnsearch or custom distance matrix computations that form the algorithm's core. Additionally, we explore the algorithm's limitations, such as computational complexity with large datasets and sensitivity to feature scaling, along with improvement methods like dimensionality reduction techniques and parameter optimization strategies. This comprehensive coverage will help readers better understand and apply KNN algorithm in practical projects. Through study and practice, readers will master the core principles and implementation techniques of KNN algorithm, enabling more effective application in real-world scenarios. The provided MATLAB code demonstrates practical implementation aspects including data preprocessing, neighbor selection logic, and majority voting mechanisms for classification tasks.