Update the version and the README file

Author: Guillaume Lemaitre

README.md

@@ -18,7 +18,9 @@ Installation
 
 ### Dependencies
 
-* scipy(>=0.17.1)
+UnbalancedDataset is tested to work under Python 2.7 and Python 3.5.
+
+* scipy(>=0.17.0)
 * numpy(>=1.10.4)
 * scikit-learn(>=0.17.1)
 
@@ -36,6 +38,14 @@ copy from Github and install all dependencies:
     cd UnbalancedDataset
     python setup.py install
 
+### Testing
+
+After installation, you can use `nose` to run the test suite:
+
+```
+make coverage
+```
+
 About
 =====
 
@@ -46,45 +56,53 @@ One way of addresing this issue is by re-sampling the dataset as to offset this
 Re-sampling techniques are divided in two 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.
 
 Bellow 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
+    2. [Extraction of majority-minority Tomek links][1]
     3. Under-sampling with Cluster Centroids
-    4. NearMiss-(1 & 2 & 3)
-    5. Condensend Nearest Neighbour
-    6. One-Sided Selection
-    7. Neighboorhood Cleaning Rule
+    4. [NearMiss-(1 & 2 & 3)][2]
+    5. [Condensend Nearest Neighbour][3]
+    6. [One-Sided Selection][4]
+    7. [Neighboorhood Cleaning Rule][5]
+	8. [Edited Nearest Neighbours][6]
+	9. [Instance Hardness Threshold][7]
 
 * Over-sampling
     1. Random minority over-sampling with replacement
-    2. SMOTE - Synthetic Minority Over-sampling Technique
-    3. bSMOTE(1&2) - Borderline SMOTE of types 1 and 2
-    4. SVM_SMOTE - Support Vectors SMOTE
+    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]
 
 * Over-sampling followed by under-sampling
-    1. SMOTE + Tomek links
-    2. SMOTE + ENN
+    1. [SMOTE + Tomek links][12]
+    2. [SMOTE + ENN][11]
 
 * Ensemble sampling
-    1. EasyEnsemble
-    2. BalanceCascade
+    1. [EasyEnsemble][13]
+    2. [BalanceCascade][13]
 
 The different algorithms are presented in the [following notebook](https://github.com/fmfn/UnbalancedDataset/blob/master/examples/plot_unbalanced_dataset.ipynb).
 
 This is a work in progress. Any comments, suggestions or corrections are welcome.
 
 References:
-
-1. NearMiss - ["kNN approach to unbalanced data distributions: A case study involving information extraction"](http://web0.site.uottawa.ca:4321/~nat/Workshop2003/jzhang.pdf), by Zhang et al., 2003.
-1. CNN - ["Addressing the Curse of Imbalanced Training Sets: One-Sided Selection"](http://sci2s.ugr.es/keel/pdf/algorithm/congreso/kubat97addressing.pdf), by Kubat et al., 1997.
-1. One-Sided Selection - ["Addressing the Curse of Imbalanced Training Sets: One-Sided Selection"](http://sci2s.ugr.es/keel/pdf/algorithm/congreso/kubat97addressing.pdf), by Kubat et al., 1997.
-1. NCL - ["Improving identification of difficult small classes by balancing class distribution"](http://sci2s.ugr.es/keel/pdf/algorithm/congreso/2001-Laurikkala-LNCS.pdf), by Laurikkala et al., 2001.
-1. SMOTE - ["SMOTE: synthetic minority over-sampling technique"](https://www.jair.org/media/953/live-953-2037-jair.pdf), by Chawla et al., 2002.
-1. Borderline SMOTE -  ["Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning"](http://sci2s.ugr.es/keel/keel-dataset/pdfs/2005-Han-LNCS.pdf), by Han et al., 2005
-1. SVM_SMOTE - ["Borderline Over-sampling for Imbalanced Data Classification"](https://www.google.fr/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CDAQFjABahUKEwjH7qqamr_HAhWLthoKHUr0BIo&url=http%3A%2F%2Fousar.lib.okayama-u.ac.jp%2Ffile%2F19617%2FIWCIA2009_A1005.pdf&ei=a7zZVYeNDIvtasrok9AI&usg=AFQjCNHoQ6oC_dH1M1IncBP0ZAaKj8a8Cw&sig2=lh32CHGjs5WBqxa_l0ylbg), Nguyen et al., 2011.
-1. SMOTE + Tomek - ["Balancing training data for automated annotation of keywords: a case study"](http://www.icmc.usp.br/~gbatista/files/wob2003.pdf), Batista et al., 2003.
-1. SMOTE + ENN - ["A study of the behavior of several methods for balancing machine learning training data"](http://www.sigkdd.org/sites/default/files/issues/6-1-2004-06/batista.pdf), Batista et al., 2004.
-1. EasyEnsemble & BalanceCascade - ["Exploratory Understanding for Class-Imbalance Learning"](http://cse.seu.edu.cn/people/xyliu/publication/tsmcb09.pdf), by Liu et al., 2009.
+===========
+
+[1]: I. Tomek, [“Two modifications of CNN,”](http://sci2s.ugr.es/keel/pdf/algorithm/articulo/1976-Tomek-IEEETSMC(2).pdf) In Systems, Man, and Cybernetics, IEEE Transactions on, vol. 6, pp 769-772, 2010.
+[2]: I. Mani, I. Zhang. [“kNN approach to unbalanced data distributions: a case study involving information extraction,”](http://web0.site.uottawa.ca:4321/~nat/Workshop2003/jzhang.pdf) In Proceedings of workshop on learning from imbalanced datasets, 2003.
+[3]: P. Hart, [“The condensed nearest neighbor rule,”](http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1054155&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1054155) In Information Theory, IEEE Transactions on, vol. 14(3), pp. 515-516, 1968.
+[4]: M. Kubat, S. Matwin, [“Addressing the curse of imbalanced training sets: one-sided selection,”](http://sci2s.ugr.es/keel/pdf/algorithm/congreso/kubat97addressing.pdf) In ICML, vol. 97, pp. 179-186, 1997.
+[5]: J. Laurikkala, [“Improving identification of difficult small classes by balancing class distribution,”](http://sci2s.ugr.es/keel/pdf/algorithm/congreso/2001-Laurikkala-LNCS.pdf) Springer Berlin Heidelberg, 2001.
+[6]: D. Wilson, [“Asymptotic Properties of Nearest Neighbor Rules Using Edited Data,”](http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4309137&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D4309137) In IEEE Transactions on Systems, Man, and Cybernetrics, vol. 2 (3), pp. 408-421, 1972.
+[7]: D. Smith, Michael R., Tony Martinez, and Christophe Giraud-Carrier. [“An instance level analysis of data complexity.”](http://axon.cs.byu.edu/papers/smith.ml2013.pdf) Machine learning 95.2 (2014): 225-256.
+[8]: N. V. Chawla, K. W. Bowyer, L. O.Hall, W. P. Kegelmeyer, [“SMOTE: synthetic minority over-sampling technique,”](https://www.jair.org/media/953/live-953-2037-jair.pdf) Journal of artificial intelligence research, 321-357, 2002.
+[9]: H. Han, W. Wen-Yuan, M. Bing-Huan, [“Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning,”](http://sci2s.ugr.es/keel/keel-dataset/pdfs/2005-Han-LNCS.pdf) Advances in intelligent computing, 878-887, 2005.
+[10]: H. M. Nguyen, E. W. Cooper, K. Kamei, [“Borderline over-sampling for imbalanced data classification,”](https://www.google.fr/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CDAQFjABahUKEwjH7qqamr_HAhWLthoKHUr0BIo&url=http%3A%2F%2Fousar.lib.okayama-u.ac.jp%2Ffile%2F19617%2FIWCIA2009_A1005.pdf&ei=a7zZVYeNDIvtasrok9AI&usg=AFQjCNHoQ6oC_dH1M1IncBP0ZAaKj8a8Cw&sig2=lh32CHGjs5WBqxa_l0ylbg) International Journal of Knowledge Engineering and Soft Data Paradigms, 3(1), pp.4-21, 2001.
+[11]: G. Batista, R. C. Prati, M. C. Monard. [“A study of the behavior of several methods for balancing machine learning training data,”](http://www.sigkdd.org/sites/default/files/issues/6-1-2004-06/batista.pdf) ACM Sigkdd Explorations Newsletter 6 (1), 20-29, 2004.
+[12]: G. Batista, B. Bazzan, M. Monard, [“Balancing Training Data for Automated Annotation of Keywords: a Case Study,”)[(http://www.icmc.usp.br/~gbatista/files/wob2003.pdf)] In WOB, 10-18, 2003.
+[13]: X. Y. Liu, J. Wu and Z. H. Zhou, [“Exploratory Undersampling for Class-Imbalance Learning,”](http://cse.seu.edu.cn/people/xyliu/publication/tsmcb09.pdf) in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 2, pp. 539-550, April 2009.

unbalanced_dataset/version.py

@@ -35,7 +35,7 @@
         'required_at_installation': True,
         'install_info': _UNBALANCED_DATASET_INSTALL_MSG}),
     ('scipy', {
-        'min_version': '0.17.1',
+        'min_version': '0.17.0',
         'required_at_installation': True,
         'install_info': _UNBALANCED_DATASET_INSTALL_MSG}),
     ('sklearn', {