MNT update README file

Author: glemaitre

README.rst

@@ -41,7 +41,7 @@ Documentation
 Installation documentation, API documentation, and examples can be found on the
 documentation_.
 
-.. _documentation: https://imbalanced-learn.readthedocs.io/en/stable/
+.. _documentation: https://imbalanced-learn.org/stable/
 
 Installation
 ------------
@@ -150,9 +150,11 @@ Below is a list of the methods currently implemented in this module.
 * Over-sampling
     1. Random minority over-sampling with replacement
     2. SMOTE - Synthetic Minority Over-sampling Technique [8]_
-    3. bSMOTE(1 & 2) - Borderline SMOTE of types 1 and 2 [9]_
-    4. SVM SMOTE - Support Vectors SMOTE [10]_
-    5. ADASYN - Adaptive synthetic sampling approach for imbalanced learning [15]_
+    3. SMOTENC - SMOTE for Nominal Continuous [8]_
+    4. bSMOTE(1 & 2) - Borderline SMOTE of types 1 and 2 [9]_
+    5. SVM SMOTE - Support Vectors SMOTE [10]_
+    6. ADASYN - Adaptive synthetic sampling approach for imbalanced learning [15]_
+    7. KMeans-SMOTE [17]_
 
 * Over-sampling followed by under-sampling
     1. SMOTE + Tomek links [12]_
@@ -162,6 +164,9 @@ Below is a list of the methods currently implemented in this module.
     1. Easy Ensemble classifier [13]_
     2. Balanced Random Forest [16]_
     3. Balanced Bagging
+    4. RUSBoost [18]_
+
+* Mini-batch resampling for Keras and Tensorflow
 
 The different algorithms are presented in the sphinx-gallery_.
 
@@ -202,3 +207,7 @@ References:
 .. [15] : H. He, Y. Bai, E. A. Garcia, S. Li, “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” In Proceedings of the 5th IEEE International Joint Conference on Neural Networks, pp. 1322-1328, 2008.
 
 .. [16] : C. Chao, A. Liaw, and L. Breiman. "Using random forest to learn imbalanced data." University of California, Berkeley 110 (2004): 1-12.
+
+.. [17] : Felix Last, Georgios Douzas, Fernando Bacao, "Oversampling for Imbalanced Learning Based on K-Means and SMOTE"
+
+.. [18] : Seiffert, C., Khoshgoftaar, T. M., Van Hulse, J., & Napolitano, A. "RUSBoost: A hybrid approach to alleviating class imbalance." IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 40.1 (2010): 185-197.