Class Distribution Estimation

Class distribution estimation aims to determine the probabilities of different classes in a dataset where class labels are unavailable, often relying on assumptions about the relationship between training and test data distributions. Recent research focuses on developing robust and efficient algorithms, including adaptations of Support Vector Machines and Expectation-Maximization methods, that handle various scenarios such as imbalanced data, sparse features, and deviations from standard assumptions like covariate shift. These advancements are crucial for improving the reliability and performance of machine learning models in diverse applications, particularly in areas like healthcare and image analysis where labeled data is scarce or costly to obtain.