K Nearest Neighbor

K-Nearest Neighbors (KNN) is a fundamental machine learning algorithm that classifies data points based on the majority class among their closest neighbors. Current research focuses on improving KNN's efficiency and accuracy through modifications like incorporating weighted neighbors based on information theory, developing hybrid models combining KNN with other techniques (e.g., neural networks, gradient boosting), and addressing challenges such as handling noisy data, concept drift in data streams, and mitigating vulnerabilities to data poisoning attacks. These advancements enhance KNN's applicability across diverse fields, including air quality prediction, anomaly detection in videos, recommender systems, and biomedical image analysis.