5. Ensemble of samplers#

5.1. Classifier including inner balancing samplers#

5.1.1. Bagging classifier#

In ensemble classifiers, bagging methods build several estimators on different randomly selected subset of data. In scikit-learn, this classifier is named BaggingClassifier. However, this classifier does not allow to balance each subset of data. Therefore, when training on imbalanced data set, this classifier will favor the majority classes:

>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=10000, n_features=2, n_informative=2,
...                            n_redundant=0, n_repeated=0, n_classes=3,
...                            n_clusters_per_class=1,
...                            weights=[0.01, 0.05, 0.94], class_sep=0.8,
...                            random_state=0)
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.metrics import balanced_accuracy_score
>>> from sklearn.ensemble import BaggingClassifier
>>> from sklearn.tree import DecisionTreeClassifier
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
>>> bc = BaggingClassifier(base_estimator=DecisionTreeClassifier(),
...                        random_state=0)
>>> bc.fit(X_train, y_train) 
>>> y_pred = bc.predict(X_test)
>>> balanced_accuracy_score(y_test, y_pred)  

In BalancedBaggingClassifier, each bootstrap sample will be further resampled to achieve the sampling_strategy desired. Therefore, BalancedBaggingClassifier takes the same parameters as the scikit-learn BaggingClassifier. In addition, the sampling is controlled by the parameter sampler or the two parameters sampling_strategy and replacement, if one wants to use the RandomUnderSampler:

>>> from imblearn.ensemble import BalancedBaggingClassifier
>>> bbc = BalancedBaggingClassifier(base_estimator=DecisionTreeClassifier(),
...                                 sampling_strategy='auto',
...                                 replacement=False,
...                                 random_state=0)
>>> bbc.fit(X_train, y_train) 
>>> y_pred = bbc.predict(X_test)
>>> balanced_accuracy_score(y_test, y_pred)  

Changing the sampler will give rise to different known implementation [MO97], [HKT09], [WY09]. You can refer to the following example shows in practice these different methods: Bagging classifiers using sampler

5.1.2. Forest of randomized trees#

BalancedRandomForestClassifier is another ensemble method in which each tree of the forest will be provided a balanced bootstrap sample [CLB+04]. This class provides all functionality of the RandomForestClassifier:

>>> from imblearn.ensemble import BalancedRandomForestClassifier
>>> brf = BalancedRandomForestClassifier(n_estimators=100, random_state=0)
>>> brf.fit(X_train, y_train) 
>>> y_pred = brf.predict(X_test)
>>> balanced_accuracy_score(y_test, y_pred)  

5.1.3. Boosting#

Several methods taking advantage of boosting have been designed.

RUSBoostClassifier randomly under-sample the dataset before to perform a boosting iteration [SKVHN09]:

>>> from imblearn.ensemble import RUSBoostClassifier
>>> rusboost = RUSBoostClassifier(n_estimators=200, algorithm='SAMME.R',
...                               random_state=0)
>>> rusboost.fit(X_train, y_train)  
>>> y_pred = rusboost.predict(X_test)
>>> balanced_accuracy_score(y_test, y_pred)  

A specific method which uses AdaBoostClassifier as learners in the bagging classifier is called “EasyEnsemble”. The EasyEnsembleClassifier allows to bag AdaBoost learners which are trained on balanced bootstrap samples [LWZ08]. Similarly to the BalancedBaggingClassifier API, one can construct the ensemble as:

>>> from imblearn.ensemble import EasyEnsembleClassifier
>>> eec = EasyEnsembleClassifier(random_state=0)
>>> eec.fit(X_train, y_train) 
>>> y_pred = eec.predict(X_test)
>>> balanced_accuracy_score(y_test, y_pred)