Add documentation for SMOTEN

Author: ThomasKluiters

doc/over_sampling.rst

@@ -183,7 +183,7 @@ parameters ``categorical_features`` either by passing the indices of these
 features or a boolean mask marking these features::
 
   >>> from imblearn.over_sampling import SMOTENC
-  >>> smote_nc = SMOTENC(categorical_features=[0, 2], random_state=0)
+  >>> smote_nc = SMOTEN(categorical_features=[0, 2], random_state=0)
   >>> X_resampled, y_resampled = smote_nc.fit_resample(X, y)
   >>> print(sorted(Counter(y_resampled).items()))
   [(0, 30), (1, 30)]
@@ -198,6 +198,15 @@ Therefore, it can be seen that the samples generated in the first and last
 columns are belonging to the same categories originally presented without any
 other extra interpolation.
 
+Furthermore, if the dataset solely consists of categorical features one may use the :class:`SMOTEN` class. This class generates samples in an identical fashion to :class:`SMOTENC` - however - only categorical features are permitted. Each feature is treated as a categorical feature and therefore it is not advised to use `SMOTEN` for datasets that contain both categorical and continious features::
+
+  >>> from imblearn.over_sampling import SMOTEN
+  >>> smote_n = SMOTEN(random_state=0)
+  >>> X[:, 1] = rng.randint(2, size=n_samples)
+  >>> X_resampled, y_resampled = smote_n.fit_resample(X, y)
+  >>> print(sorted(Counter(y_resampled).items()))
+  [(0, 30), (1, 30)]
+
 .. topic:: References
 
   .. [HWB2005] H. Han, W. Wen-Yuan, M. Bing-Huan, "Borderline-SMOTE: a new

doc/whats_new/v0.5.rst

@@ -27,6 +27,9 @@ Enhancement
   and issue template showing how to print system and dependency information
   from the command line. :issue:`557` by :user:`Alexander L. Hayes <batflyer>`.
 
+- Add :class:`SMOTEN`. Add ability to use SMOTE on pure categorical features.
+  by :user:`Thomas Kluiters <ThomasKluiters`.
+
 Maintenance
 ...........