Package: ebmc
Type: Package
Title: Ensemble-Based Methods for Class Imbalance Problem
Version: 1.0.1
Author: Hsiang Hao, Chen
Maintainer: "Hsiang Hao, Chen" <kbman1101@gmail.com>
Description: Four ensemble-based methods (SMOTEBoost, RUSBoost,
        UnderBagging, and SMOTEBagging) for class imbalance problem are
        implemented for binary classification. Such methods adopt
        ensemble methods and data re-sampling techniques to improve
        model performance in presence of class imbalance problem. One
        special feature offers the possibility to choose multiple
        supervised learning algorithms to build weak learners within
        ensemble models. References: Nitesh V. Chawla, Aleksandar
        Lazarevic, Lawrence O. Hall, and Kevin W. Bowyer (2003)
        <doi:10.1007/978-3-540-39804-2_12>, Chris Seiffert, Taghi M.
        Khoshgoftaar, Jason Van Hulse, and Amri Napolitano (2010)
        <doi:10.1109/TSMCA.2009.2029559>, R. Barandela, J. S. Sanchez,
        R. M. Valdovinos (2003) <doi:10.1007/s10044-003-0192-z>, Shuo
        Wang and Xin Yao (2009) <doi:10.1109/CIDM.2009.4938667>, Yoav
        Freund and Robert E. Schapire (1997)
        <doi:10.1006/jcss.1997.1504>.
Depends: methods
Imports: e1071, rpart, C50, randomForest, pROC, smotefamily
License: GPL (>= 3)
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2026-09-22 09:31:44 UTC; root
Repository: https://cran.r-universe.dev
Date/Publication: 2022-01-10 17:12:44 UTC
RemoteUrl: https://github.com/cran/ebmc
RemoteRef: HEAD
RemoteSha: c70b4c5b8bb495bef2be180fe247d6217d244167