{ "_id": "6ab24bb63d719c1f8408b680", "Package": "ebmc", "Type": "Package", "Title": "Ensemble-Based Methods for Class Imbalance Problem", "Version": "1.0.1", "Author": "Hsiang Hao, Chen", "Maintainer": "\"Hsiang Hao, Chen\" ", "Description": "Four ensemble-based methods (SMOTEBoost, RUSBoost,\nUnderBagging, and SMOTEBagging) for class imbalance problem are\nimplemented for binary classification. Such methods adopt\nensemble methods and data re-sampling techniques to improve\nmodel performance in presence of class imbalance problem. One\nspecial feature offers the possibility to choose multiple\nsupervised learning algorithms to build weak learners within\nensemble models. References: Nitesh V. Chawla, Aleksandar\nLazarevic, Lawrence O. Hall, and Kevin W. Bowyer (2003)\n, Chris Seiffert, Taghi M.\nKhoshgoftaar, Jason Van Hulse, and Amri Napolitano (2010)\n, R. Barandela, J. S. Sanchez,\nR. M. Valdovinos (2003) , Shuo\nWang and Xin Yao (2009) , Yoav\nFreund and Robert E. 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