Python Imbalanced learning package. To improve results or speed of learning process in Machine Learning algorithms on datasets where one or more of the classes has significantly less / more training examples you can use imbalanced learning approach. Imbalanced learning methods use re-sampling techniques like SMOTE, ADASYN, Tomek links, and their various combinations.
imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance. It is compatible with scikit-learn and is part of scikit-learn-contrib projects.
Most classification algorithms will only perform optimally when the number of samples of each class is roughly the same. Highly skewed datasets, where the minority is heavily outnumbered by one or more classes, have proven to be a challenge while at the same time becoming more and more common.
One way of addressing this issue is by re-sampling the dataset as to offset this imbalance with the hope of arriving at a more robust and fair decision boundary than you would otherwise.
Re-sampling techniques are divided in two categories:
Under-sampling the majority class(es).
Over-sampling the minority class.
Combining over- and under-sampling.
Create ensemble balanced sets.
Below is a list of the methods currently implemented in this module.
Under-sampling
- Random majority under-sampling with replacement
- Extraction of majority-minority Tomek links
- Under-sampling with Cluster Centroids
- NearMiss-(1 & 2 & 3)
- Condensed Nearest Neighbour
- One-Sided Selection
- Neighboorhood Cleaning Rule
- Edited Nearest Neighbours
- Instance Hardness Threshold
- Repeated Edited Nearest Neighbours
- AllKNN
Over-sampling 12. Random minority over-sampling with replacement 13. SMOTE - Synthetic Minority Over-sampling Technique 14. bSMOTE(1 & 2) - Borderline SMOTE of types 1 and 2 15. SVM SMOTE - Support Vectors SMOTE 16. ADASYN - Adaptive synthetic sampling approach for imbalanced learning
Over-sampling followed by under-sampling
- SMOTE + Tomek links
- SMOTE + ENN
Ensemble classifier using samplers internally
- EasyEnsemble
- BalanceCascade
- Balanced Random Forest
- Balanced Bagging
