#Imbalanced Learning

An extensive collection of imbalanced learning materials, including curated papers, codes, and libraries. The repository classifies frameworks and libraries by programming language, from Python to Julia, and organizes research papers by domain, such as ensemble and deep learning. Keep informed with the latest updates, featuring the 'imbalanced-ensemble' package, and explore a range of algorithms for multi-class imbalanced learning, with features like parallel execution and compatibility with popular libraries like scikit-learn. This carefully selected non-exhaustive collection supports unbiased model learning from imbalanced datasets.