costsensitive: Cost-Sensitive Multi-Class Classification

Reduction-based techniques for cost-sensitive multi-class classification, in which each observation has a different cost for classifying it into one class, and the goal is to predict the class with the minimum expected cost for each new observation. Implements Weighted All-Pairs (Beygelzimer, A., Langford, J., & Zadrozny, B., 2008, <doi:10.1007/978-0-387-79361-0_1>), Weighted One-Vs-Rest (Beygelzimer, A., Dani, V., Hayes, T., Langford, J., & Zadrozny, B., 2005, <https://dl.acm.org/citation.cfm?id=1102358>) and Regression One-Vs-Rest. Works with arbitrary classifiers taking observation weights, or with regressors. Also implements cost-proportionate rejection sampling for working with classifiers that don't accept observation weights.

Version: 0.1.2.10
Suggests: parallel
Published: 2019-07-28
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera at gmail.com>
License: BSD_2_clause + file LICENSE
URL: https://github.com/david-cortes/costsensitive
NeedsCompilation: yes
CRAN checks: costsensitive results

Downloads:

Reference manual: costsensitive.pdf
Package source: costsensitive_0.1.2.10.tar.gz
Windows binaries: r-devel: costsensitive_0.1.2.10.zip, r-devel-gcc8: costsensitive_0.1.2.10.zip, r-release: costsensitive_0.1.2.10.zip, r-oldrel: costsensitive_0.1.2.10.zip
OS X binaries: r-release: costsensitive_0.1.2.10.tgz, r-oldrel: costsensitive_0.1.2.10.tgz
Old sources: costsensitive archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=costsensitive to link to this page.