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Expand Up @@ -58,19 +58,19 @@ Bellow is a list of the methods currently implemented in this module.
1. EasyEnsemble
2. BalanceCascade

The different algorithms are presented in the (following notebook)[https://github.com/glemaitre/UnbalancedDataset/blob/master/notebook/Notebook_UnbalancedDataset.ipynb].
The different algorithms are presented in the [following notebook](https://github.com/glemaitre/UnbalancedDataset/blob/master/notebook/Notebook_UnbalancedDataset.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., 2020.
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. 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., 2020.
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.