.. currentmodule:: imblearn.over_sampling
We previously presented :class:`SMOTE` and showed that this method can generate noisy samples by interpolating new points between marginal outliers and inliers. This issue can be solved by cleaning the space resulting from over-sampling.
.. currentmodule:: imblearn.combine
In this regard, Tomek's link and edited nearest-neighbours are the two cleaning methods that have been added to the pipeline after applying SMOTE over-sampling to obtain a cleaner space. The two ready-to use classes imbalanced-learn implements for combining over- and undersampling methods are: (i) :class:`SMOTETomek` :cite:`batista2004study` and (ii) :class:`SMOTEENN` :cite:`batista2003balancing`.
Those two classes can be used like any other sampler with parameters identical to their former samplers:
>>> from collections import Counter >>> from sklearn.datasets import make_classification >>> X, y = make_classification(n_samples=5000, 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) >>> print(sorted(Counter(y).items())) [(0, 64), (1, 262), (2, 4674)] >>> from imblearn.combine import SMOTEENN >>> smote_enn = SMOTEENN(random_state=0) >>> X_resampled, y_resampled = smote_enn.fit_resample(X, y) >>> print(sorted(Counter(y_resampled).items())) [(0, 4060), (1, 4381), (2, 3502)] >>> from imblearn.combine import SMOTETomek >>> smote_tomek = SMOTETomek(random_state=0) >>> X_resampled, y_resampled = smote_tomek.fit_resample(X, y) >>> print(sorted(Counter(y_resampled).items())) [(0, 4499), (1, 4566), (2, 4413)]
We can also see in the example below that :class:`SMOTEENN` tends to clean more noisy samples than :class:`SMOTETomek`.
