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[MRG] ENH: safe-level SMOTE #626

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[MRG] ENH: safe-level SMOTE #626

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bcc3069
added safe-level-smote method
laurallu Nov 4, 2019
394d686
unit tests added for safe-level SMOTE
laurallu Nov 6, 2019
609c4fc
fixed variable name, added doc and test
laurallu Nov 11, 2019
fd11e32
Merge remote-tracking branch 'upstream/master' into safe-level
laurallu Nov 11, 2019
866a04f
=removed redundant lines
laurallu Nov 12, 2019
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18 changes: 16 additions & 2 deletions doc/over_sampling.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -152,8 +152,9 @@ nearest neighbors class. Those variants are presented in the figure below.
:align: center


The :class:`BorderlineSMOTE` [HWB2005]_, :class:`SVMSMOTE` [NCK2009]_, and
:class:`KMeansSMOTE` [LDB2017]_ offer some variant of the SMOTE algorithm::
The :class:`BorderlineSMOTE` [HWB2005]_, :class:`SVMSMOTE` [NCK2009]_,
:class:`KMeansSMOTE` [LDB2017]_ and :class:`SafeLevelSMOTE` [BSL2009]_
offer some variant of the SMOTE algorithm::

>>> from imblearn.over_sampling import BorderlineSMOTE
>>> X_resampled, y_resampled = BorderlineSMOTE().fit_resample(X, y)
Expand Down Expand Up @@ -213,6 +214,14 @@ other extra interpolation.
Imbalanced Learning Based on K-Means and SMOTE"
https://arxiv.org/abs/1711.00837

[BSL2009] C. Bunkhumpornpat, K. Sinapiromsaran, C. Lursinsap,
"Safe-level-SMOTE: Safe-level-synthetic minority over-sampling
technique for handling the class imbalanced problem," In:
Theeramunkong T., Kijsirikul B., Cercone N., Ho TB. (eds)
Advances in Knowledge Discovery and Data Mining. PAKDD 2009.
Lecture Notes in Computer Science, vol 5476. Springer, Berlin,
Heidelberg, 475-482, 2009.

Mathematical formulation
========================

Expand Down Expand Up @@ -274,6 +283,11 @@ parameter ``m_neighbors`` to decide if a sample is in danger, safe, or noise.
method before to apply SMOTE. The clustering will group samples together and
generate new samples depending of the cluster density.

**SafeLevel** SMOTE --- cf. to :class:`SafeLevelSMOTE` --- uses the safe level
(the number of positive instances in nearest neighbors) to generate a synthetic
instance. Compared to regular SMOTE, the new instance is positioned closer to
the positive instance with larger safe level.

ADASYN works similarly to the regular SMOTE. However, the number of
samples generated for each :math:`x_i` is proportional to the number of samples
which are not from the same class than :math:`x_i` in a given
Expand Down
2 changes: 2 additions & 0 deletions imblearn/over_sampling/__init__.py
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Expand Up @@ -10,6 +10,7 @@
from ._smote import KMeansSMOTE
from ._smote import SVMSMOTE
from ._smote import SMOTENC
from ._smote import SafeLevelSMOTE

__all__ = [
"ADASYN",
Expand All @@ -19,4 +20,5 @@
"BorderlineSMOTE",
"SVMSMOTE",
"SMOTENC",
"SafeLevelSMOTE",
]
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