Version 0.5 (under development)¶
Fix wrong usage of
porto_seguro_keras_under_sampling.pyexample. The batch normalization was moved before the activation function and the bias was removed from the dense layer. #531 by Guillaume Lemaitre.
Version 0.4 is the last version of imbalanced-learn to support Python 2.7 and Python 3.4. Imbalanced-learn 0.5 will require Python 3.5 or higher.
This release brings its set of new feature as well as some API changes to strengthen the foundation of imbalanced-learn.
imblearn.ensemble has been consolidated with new classifier:
Support for string has been added in
imblearn.under_sampling.RandomUnderSampler. In addition, a new class
imblearn.over_sampling.SMOTENC allows to generate sample with data
sets containing both continuous and categorical features.
There is also some changes regarding the API:
sampling_strategy has been introduced to replace the
ratio parameter. In addition, the
return_indices argument has been
deprecated and all samplers will exposed a
sample_indices_ whenever this is
fit_resample. An alias is still available for backward compatibility. In addition,
samplehas been removed to avoid resampling on different set of data. #462 by Guillaume Lemaitre.
imblearn.over_sampling.SMOTE. User should use
imblearn.over_sampling.BorderlineSMOTE. #440 by Guillaume Lemaitre.
All the unit tests have been factorized and a
utils.check_estimatorshas been derived from scikit-learn. By Guillaume Lemaitre.
Fixed a bug in
under_sampling.RepeatedEditedNearestNeighbours, add additional stopping criterion to avoid that the minority class become a majority class or that a class disappear. By Guillaume Lemaitre.
Fixed a bug in
under_sampling.CondensedNeareastNeigbour, correction of the list of indices returned. By Guillaume Lemaitre.
Fixed a bug in
under_sampling.CondensedNeareastNeigbour, correction of the shape of sel_x when only one sample is selected. By Aliaksei Halachkin.
Added AllKNN under sampling technique. By Dayvid Oliveira.
Added support for bumpversion. By Guillaume Lemaitre.
Move random_state to be assigned in the
SamplerMixininitialization. By Guillaume Lemaitre.
n_neighbors accept KNeighborsMixin based object for
under_sampling.AllKNN. #109 by Guillaume Lemaitre.
Random majority under-sampling with replacement
Extraction of majority-minority Tomek links
Under-sampling with Cluster Centroids
NearMiss-(1 & 2 & 3)
Condensend Nearest Neighbour
Neighboorhood Cleaning Rule
Edited Nearest Neighbours
Instance Hardness Threshold
Repeated Edited Nearest Neighbours
Random minority over-sampling with replacement
SMOTE - Synthetic Minority Over-sampling Technique
bSMOTE(1 & 2) - Borderline SMOTE of types 1 and 2
SVM SMOTE - Support Vectors SMOTE
ADASYN - Adaptive synthetic sampling approach for imbalanced learning
- Over-sampling followed by under-sampling
SMOTE + Tomek links
SMOTE + ENN
- Ensemble sampling