MATLAB Implementation of K-Nearest Neighbors Classification Algorithm

In this article, I will demonstrate how to implement a classification program using the K-Nearest Neighbors (KNN) algorithm in MATLAB. We will begin with data preprocessing stages, including data cleaning, normalization through functions like zscore or mapminmax, and feature selection techniques such as sequential feature selection. Then we'll explore the fundamental principles of KNN algorithm and its implementation using MATLAB's fitcknn function, which handles distance metrics (Euclidean, Manhattan, Cosine) and neighbor weighting.

Next, we will discuss performance evaluation methods for classification algorithms, introducing key metrics like confusion matrices generated using confusionmat function, accuracy rates, precision, recall, and F1-score calculations. We'll demonstrate cross-validation techniques using cvpartition and crossval functions to ensure robust model evaluation.

Throughout the programming process, we will utilize MATLAB's Machine Learning Toolbox, which provides essential functions for classification, regression, and clustering tasks. We'll implement practical examples using popular datasets like the Iris dataset (accessible through fisheriris) and handwritten digits dataset (available via digitDataset), helping you better understand program implementation and performance assessment.

By studying this article, you will learn how to implement KNN classification, master MATLAB programming for classification tasks, and evaluate algorithm performance effectively. Additionally, you'll gain skills in interpreting and utilizing classification results through proper visualization techniques and decision boundary analysis, thereby enhancing your data analysis capabilities.