Package: ebmc 1.0.1
ebmc: Ensemble-Based Methods for Class Imbalance Problem
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>.
Authors:
ebmc_1.0.1.tar.gz
ebmc_1.0.1.tar.gz(r-4.5-noble)ebmc_1.0.1.tar.gz(r-4.4-noble)
ebmc_1.0.1.tgz(r-4.4-emscripten)ebmc_1.0.1.tgz(r-4.3-emscripten)
ebmc.pdf |ebmc.html✨
ebmc/json (API)
# Install 'ebmc' in R: |
install.packages('ebmc', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:c70b4c5b8b. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 23 2024 |
R-4.5-linux | OK | Dec 23 2024 |
Exports:adam2measurepredict.modelBagpredict.modelBstrussbagsboub
Dependencies:C50classclicpp11Cubistdbscane1071FNNFormulagenericsglueigraphinumlatticelibcoinlifecyclemagrittrMASSMatrixmvtnormpartykitpkgconfigplyrpROCproxyrandomForestRcppreshape2rlangrpartsmotefamilystringistringrsurvivalvctrs
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Implementation of AdaBoost.M2 | adam2 |
Calculating Performance Measurement in Class Imbalance Problem | measure |
Predict Method for modelBag Object | predict.modelBag |
Predict Method for modelBst Object | predict.modelBst |
Implementation of RUSBoost | rus |
Implementation of SMOTEBagging | sbag |
Implementation of SMOTEBoost | sbo |
Implementation of UnderBagging | ub |