From dd488acd43cd441052694147b632d565404a0818 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Wed, 22 Aug 2018 23:14:29 +0200 Subject: [PATCH] DOC: mention EasyEnsemble in BalancedBaggingClassifier --- imblearn/ensemble/classifier.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/imblearn/ensemble/classifier.py b/imblearn/ensemble/classifier.py index 62d06c36b..06f650126 100644 --- a/imblearn/ensemble/classifier.py +++ b/imblearn/ensemble/classifier.py @@ -23,7 +23,8 @@ sampling_strategy=BaseUnderSampler._sampling_strategy_docstring, random_state=_random_state_docstring) class BalancedBaggingClassifier(BaggingClassifier): - """A Bagging classifier with additional balancing. + """A Bagging classifier with additional balancing. It is similar to + ``EasyEnsemble`` [6]_. This implementation of Bagging is similar to the scikit-learn implementation. It includes an additional step to balance the training set @@ -146,6 +147,10 @@ class BalancedBaggingClassifier(BaggingClassifier): .. [5] Chen, Chao, Andy Liaw, and Leo Breiman. "Using random forest to learn imbalanced data." University of California, Berkeley 110, 2004. + .. [6] X. Y. Liu, J. Wu and Z. H. Zhou, "Exploratory Undersampling for + Class-Imbalance Learning," in IEEE Transactions on Systems, Man, and + Cybernetics, Part B (Cybernetics), vol. 39, no. 2, pp. 539-550, + April 2009. Examples --------