MATLAB Implementation of K-Nearest Neighbors (KNN) Classifier

The K-Nearest Neighbors (KNN) classifier is a simple yet effective machine learning algorithm suitable for classification tasks. In MATLAB, KNN implementation can be achieved through built-in functions or custom code approaches.

### Algorithm Overview The KNN classifier operates on a fundamental assumption: similar data points tend to belong to the same category. The workflow consists of: Training Phase: Store all training samples and their labels (no explicit training process occurs). Prediction Phase: For a new sample, calculate its distance (e.g., Euclidean distance) to all training samples and identify the K closest neighbors. Voting Decision: Determine the new sample's category through majority voting among the K neighbors' labels (for K=1, this becomes a simple nearest neighbor classifier).

### MATLAB Implementation Approaches MATLAB provides two primary implementation methods: Built-in Function: The `fitcknn` function directly trains KNN models with customizable parameters including number of neighbors (K), distance metrics, and search methods. Custom Implementation: For greater flexibility, manually code distance calculations and voting logic using MATLAB's matrix operations for performance optimization. Key functions include `pdist2` for distance computation and `mode` for majority voting.

### Important Considerations Data Standardization: KNN is sensitive to feature scales; preprocess data using standardization functions like `zscore`. K Value Selection: Small K values may cause overfitting, while large K might ignore local patterns. Use cross-validation techniques with `crossval` function for optimal K selection. Computational Efficiency: For large datasets, brute-force neighbor search becomes inefficient. Consider KD-tree acceleration structures by setting the `'NSMethod'` parameter in `fitcknn` to `'kdtree'`.

With proper parameter tuning, KNN effectively solves classification problems in MATLAB, particularly suitable for scenarios with low feature dimensions and clear sample distribution patterns. The algorithm's simplicity makes it ideal for prototyping and educational purposes while offering robust performance when properly configured.