Entropy-based Sampling Approaches for Multi-Class Imbalanced Problems

lemaitre, 2017, Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning, J Mach Learn Res, 18, 1

10.1016/j.fss.2007.12.023

wang, 2018, Detecting coherent groups in crowd scenes by multiview clustering, IEEE Trans Pattern Anal Mach Intell

10.1109/TNNLS.2018.2868836

10.1109/TCYB.2018.2878977

10.1109/TSMC.2018.2790914

alcala-fdez, 2011, KEEL data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework, J Multiple-Valued Logic Soft Comput, 17, 255

10.3233/IFS-162150

10.1197/jamia.M1733

mackay, 2003, Information Theory Inference and Learning Algorithms

10.1007/978-3-642-01307-2_43

he, 2008, ADASYN: Adaptive synthetic sampling approach for imbalanced learning, Proc IEEE Int Joint Conf Neural Netw, 1322

10.1109/TNN.2010.2066988

10.1109/TKDE.2012.232

han, 2005, Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning, Proc Int Conf Intell Comput, 878

10.1109/TKDE.2017.2761347

10.1109/IJCNN.2018.8489729

liu, 2009, Exploratory undersampling for class-imbalance learning, IEEE Trans Syst Man Cybern B (Cybern ), 39, 539, 10.1109/TSMCB.2008.2007853

10.1109/TCYB.2016.2606104

10.1109/TCYB.2016.2579658

10.1007/978-1-4419-7970-4

10.1109/TKDE.2017.2682249

10.1109/TKDE.2018.2792021

10.1109/TKDE.2017.2779849

10.1613/jair.953

10.1017/CBO9780511976667

10.1109/TSG.2018.2879572

10.1109/TSE.2017.2731766

10.1016/j.patcog.2017.07.024

10.1109/TBDATA.2017.2689790

10.1109/TBDATA.2018.2884486

10.1109/TKDE.2008.239

10.1109/TKDE.2014.2345380

10.1109/ICPR.2008.4761297

10.1109/CIDM.2009.4938667

10.1016/j.patcog.2017.06.019

lazarevic, 2002, SMOTE-Boost: Improving the prediction of minority class in boosting, Proc 7th Eur Conf Principles Practice Knowl Discovery Databases, 107

10.1145/584091.584093

10.1145/1007730.1007735