DOC update README to forward to the documentation page

Author: glemaitre

README.rst

@@ -154,92 +154,7 @@ One way of addressing this issue is by re-sampling the dataset as to offset this
 imbalance with the hope of arriving at a more robust and fair decision boundary
 than you would otherwise.
 
-Re-sampling techniques are divided in following four categories:
-    1. Under-sampling the majority class(es).
-    2. Over-sampling the minority class.
-    3. Combining over- and under-sampling.
-    4. Create ensemble balanced sets.
-
-Below is a list of the methods currently implemented in this module.
-
-* Under-sampling
-    1. Random majority under-sampling with replacement
-    2. Extraction of majority-minority Tomek links [1]_
-    3. Under-sampling with Cluster Centroids
-    4. NearMiss-(1 & 2 & 3) [2]_
-    5. Condensed Nearest Neighbour [3]_
-    6. One-Sided Selection [4]_
-    7. Neighboorhood Cleaning Rule [5]_
-    8. Edited Nearest Neighbours [6]_
-    9. Instance Hardness Threshold [7]_
-    10. Repeated Edited Nearest Neighbours [14]_
-    11. AllKNN [14]_
-
-* Over-sampling
-    1. Random minority over-sampling with replacement
-    2. SMOTE - Synthetic Minority Over-sampling Technique [8]_
-    3. SMOTENC - SMOTE for Nominal and Continuous [8]_
-    4. SMOTEN - SMOTE for Nominal [8]_
-    5. bSMOTE(1 & 2) - Borderline SMOTE of types 1 and 2 [9]_
-    6. SVM SMOTE - Support Vectors SMOTE [10]_
-    7. ADASYN - Adaptive synthetic sampling approach for imbalanced learning [15]_
-    8. KMeans-SMOTE [17]_
-    9. ROSE - Random OverSampling Examples [19]_
-
-* Over-sampling followed by under-sampling
-    1. SMOTE + Tomek links [12]_
-    2. SMOTE + ENN [11]_
-
-* Ensemble classifier using samplers internally
-    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_.
-
-.. _sphinx-gallery: https://imbalanced-learn.readthedocs.io/en/stable/auto_examples/index.html
-
-
-References:
------------
-
-.. [1] : I. Tomek, “Two modifications of CNN,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 6, pp. 769-772, 1976.
-
-.. [2] : I. Mani, J. Zhang. “kNN approach to unbalanced data distributions: A case study involving information extraction,” In Proceedings of the Workshop on Learning from Imbalanced Data Sets, pp. 1-7, 2003.
-
-.. [3] : P. E. Hart, “The condensed nearest neighbor rule,” IEEE Transactions on Information Theory, vol. 14(3), pp. 515-516, 1968.
-
-.. [4] : M. Kubat, S. Matwin, “Addressing the curse of imbalanced training sets: One-sided selection,” In Proceedings of the 14th International Conference on Machine Learning, vol. 97, pp. 179-186, 1997.
-
-.. [5] : J. Laurikkala, “Improving identification of difficult small classes by balancing class distribution,” Proceedings of the 8th Conference on Artificial Intelligence in Medicine in Europe, pp. 63-66, 2001.
-
-.. [6] : D. Wilson, “Asymptotic Properties of Nearest Neighbor Rules Using Edited Data,” IEEE Transactions on Systems, Man, and Cybernetrics, vol. 2(3), pp. 408-421, 1972.
-
-.. [7] : M. R. Smith, T. Martinez, C. Giraud-Carrier, “An instance level analysis of data complexity,” Machine learning, vol. 95(2), pp. 225-256, 2014.
-
-.. [8] : N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.
-
-.. [9] : H. Han, W.-Y. Wang, B.-H. Mao, “Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning,” In Proceedings of the 1st International Conference on Intelligent Computing, pp. 878-887, 2005.
-
-.. [10] : H. M. Nguyen, E. W. Cooper, K. Kamei, “Borderline over-sampling for imbalanced data classification,” In Proceedings of the 5th International Workshop on computational Intelligence and Applications, pp. 24-29, 2009.
-
-.. [11] : G. E. A. P. A. Batista, R. C. Prati, M. C. Monard, “A study of the behavior of several methods for balancing machine learning training data,” ACM Sigkdd Explorations Newsletter, vol. 6(1), pp. 20-29, 2004.
-
-.. [12] : G. E. A. P. A. Batista, A. L. C. Bazzan, M. C. Monard, “Balancing training data for automated annotation of keywords: A case study,” In Proceedings of the 2nd Brazilian Workshop on Bioinformatics, pp. 10-18, 2003.
-
-.. [13] : X.-Y. Liu, J. Wu and Z.-H. Zhou, “Exploratory undersampling for class-imbalance learning,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 39(2), pp. 539-550, 2009.
-
-.. [14] : I. Tomek, “An experiment with the edited nearest-neighbor rule,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 6(6), pp. 448-452, 1976.
-
-.. [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.
+You can refer to the `imbalanced-learn`_ documentation to find details about
+the implemented algorithms.
 
-.. [19] : Menardi, G., Torelli, N.: "Training and assessing classification rules with unbalanced data", Data Mining and Knowledge Discovery,  28, (2014): 92–122
+.. _imbalanced-learn: https://imbalanced-learn.org/stable/user_guide.html