Package: CSMES 1.0.1

CSMES: Cost-Sensitive Multi-Criteria Ensemble Selection for Uncertain Cost Conditions

Functions for cost-sensitive multi-criteria ensemble selection (CSMES) (as described in De bock et al. (2020) <doi:10.1016/j.ejor.2020.01.052>) for cost-sensitive learning under unknown cost conditions.

Authors:Koen W. De Bock, Kristof Coussement and Stefan Lessmann

CSMES_1.0.1.tar.gz
CSMES_1.0.1.zip(r-4.5)CSMES_1.0.1.zip(r-4.4)CSMES_1.0.1.zip(r-4.3)
CSMES_1.0.1.tgz(r-4.4-any)CSMES_1.0.1.tgz(r-4.3-any)
CSMES_1.0.1.tar.gz(r-4.5-noble)CSMES_1.0.1.tar.gz(r-4.4-noble)
CSMES_1.0.1.tgz(r-4.4-emscripten)CSMES_1.0.1.tgz(r-4.3-emscripten)
CSMES.pdf |CSMES.html✨
CSMES/json (API)

# Install 'CSMES' in R:
install.packages('CSMES', repos = c('https://koendebock.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • BFP - Business failure prediction demonstration data set

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 254 downloads 6 exports 11 dependencies

Last updated 2 years agofrom:a2cd47511f. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 23 2024
R-4.5-winOKNov 23 2024
R-4.5-linuxOKNov 23 2024
R-4.4-winOKNov 23 2024
R-4.4-macOKNov 23 2024
R-4.3-winOKNov 23 2024
R-4.3-macOKNov 23 2024

Exports:brierCurveCSMES.ensNomCurveCSMES.ensSelCSMES.predictCSMES.predictParetoplotBrierCurve

Dependencies:bitopscaToolsdata.tablegplotsgtoolsKernSmoothlatticemcoROCRrpartzoo