KNN Classifier Algorithm MATLAB Implementation

This documentation provides complete MATLAB source code for implementing the KNN (k-Nearest Neighbors) classifier algorithm, offering deep insights into the algorithm's underlying mechanics and operational principles. The implementation includes core components such as distance calculation methods (Euclidean, Manhattan), neighbor selection logic, and majority voting mechanisms for classification. Through studying this source code, you will master techniques for handling feature normalization, optimizing the k parameter selection, and implementing efficient search algorithms for nearest neighbor identification. The code demonstrates practical approaches for data preprocessing, handling different distance metrics, and evaluating classification performance using confusion matrices. Additionally, we provide valuable practical tips and optimization strategies for effectively utilizing this source code, including methods for cross-validation, handling imbalanced datasets, and performance tuning for real-world applications. This resource serves as an excellent educational tool for understanding both the theoretical foundations and practical implementation aspects of the KNN algorithm in MATLAB environment.