4. Combination of over- and under-sampling

We previously presented 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 resulted space obtained after over-sampling.

In this regard, Tomek’s link and edited nearest-neighbours are the two cleaning methods which have been added pipeline after SMOTE over-sampling to obtain a cleaner space. Therefore, imbalanced-learn implemented two ready-to-use class which pipeline both over- and under-sampling methods: (i) SMOTETomek and (ii) SMOTEENN.

These two classes can be used as any other sampler with identical parameters than 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_sample(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_sample(X, y)
>>> print(sorted(Counter(y_resampled).items()))
[(0, 4499), (1, 4566), (2, 4413)]

We can also see in the example below that SMOTEENN tends to clean more noisy samples than SMOTETomek.

See SMOTE + ENN, SMOTE + Tomek, and Comparison of the combination of over- and under-sampling algorithms.