Granular Ball

Granular ball computing is a novel approach in machine learning that replaces individual data points with "granular balls," clusters of data points representing coarser-grained information. Research focuses on developing efficient and robust algorithms using granular balls within various model architectures, including support vector machines, random vector functional link networks, and deep convolutional neural networks, to improve classification, clustering, and optimization tasks. This approach offers advantages in handling noisy data, enhancing scalability for large datasets, and improving the interpretability of models, with applications spanning image processing, robotics, and other fields requiring efficient and robust data analysis.