Implementation of KNN Algorithm in MATLAB with Code Description

KNN algorithm (K-Nearest Neighbors) is an instance-based supervised learning algorithm commonly used for classification and regression tasks. Its core principle involves calculating the distance between the target sample and all samples in the training set, selecting the K nearest neighbors, and determining the target sample's class or prediction value based on these K neighbors' labels or values. When implementing KNN algorithm in MATLAB, the main workflow consists of the following steps: Data Preparation First, load and preprocess the data, typically including normalization or standardization to prevent different feature scales from affecting distance calculations. In MATLAB implementation, this can be achieved using functions like zscore for standardization or mapminmax for normalization. Distance Calculation The key aspect of KNN algorithm is calculating distances between samples. Common distance metrics include Euclidean distance, Manhattan distance, or cosine similarity. In MATLAB, efficient distance matrix computation can be performed using vectorized operations through functions like pdist2 or custom vectorized implementations for better performance. Selecting K Value The choice of K value directly impacts model performance. A small K value may make the model sensitive to noise, while a large K value might blur classification boundaries. Optimal K value is typically determined through cross-validation. MATLAB provides crossval function for implementing cross-validation procedures to evaluate different K values. Voting or Averaging For classification tasks, a majority voting mechanism is used where the most frequent class among K nearest neighbors becomes the prediction result. For regression tasks, the average value of K nearest neighbors serves as the prediction. This can be implemented using mode function for classification or mean function for regression in MATLAB. Model Evaluation Use test sets to validate model performance metrics such as accuracy, precision, and recall to ensure the KNN algorithm meets expectations. MATLAB's confusionmat function can generate confusion matrices, while custom code can calculate various evaluation metrics. The advantages of KNN algorithm include simple implementation, no training process required (lazy learning), making it suitable for small-scale datasets. However, since it requires storing all training data and calculating distances during prediction, computational complexity is high, making it unsuitable for large-scale high-dimensional data. In MATLAB, built-in matrix operations can optimize computational efficiency, or users can directly utilize the fitcknn function to call the pre-built KNN classifier with customizable parameters like distance metrics and number of neighbors.