A novel region based neighbors searching classification algorithm for big data

• The proposed Region Based Neighbors Searching Classification Algorithm (RNSCA) dynamically segments datasets into regions, enabling precise relationship analysis between test samples and existing points. • Point-wise dynamic searching reduces computational overhead by focusing on region-specific data patterns for efficient exploration. • Integration with ensemble learning enhances classification accuracy by synthesizing diverse regional insights from the dataset. • Rigorous comparisons with state-of-the-art models validate the algorithm’s superiority in handling complex datasets. The K-Nearest Neighbors (KNN) algorithm remains a cornerstone of machine learning due to its intuitive design and effectiveness in classification tasks. However, its performance often suffers from critical limitations, such as sensitivity to the choice of the parameter K and an inability to effectively capture complex relationships among neighboring…