Getting Started


Class-imbalance (also known as the long-tail problem) is the fact that the classes are not represented equally in a classification problem, which is quite common in practice. For instance, fraud detection, prediction of rare adverse drug reactions and prediction gene families. Failure to account for the class imbalance often causes inaccurate and decreased predictive performance of many classification algorithms.

Imbalanced learning (IL) aims to tackle the class imbalance problem to learn an unbiased model from imbalanced data. This is usually achieved by changing the training data distribution by resampling or reweighting. However, naive resampling or reweighting may introduce bias/variance to the training data, especially when the data has class-overlapping or contains noise.

Ensemble imbalanced learning (EIL) is known to effectively improve typical IL solutions by combining the outputs of multiple classifiers, thereby reducing the variance introduce by resampling/reweighting.

About imbalanced_ensemble

imbalanced_ensemble aims to provide users with easy-to-use EIL methods and related utilities, so that everyone can quickly deploy EIL algorithms to their tasks. The EIL methods implemented in this package have unified APIs and are compatible with other popular Python machine-learning packages such as scikit-learn and imbalanced-learn.

imbalanced_ensemble is an early version software and is under development. Any kinds of contributions are welcome!

> Note: many resampling algorithms and utilities are adapted from imbalanced-learn, which is an amazing project!