Robust multivariate classification using highly optimised SVM ensembles

A collection of functions for the creation and application of highly optimised, robustly evaluated ensembles of support vector machines (SVMs). The package takes care of training individual SVM classifiers using a fast parallel heuristic algorithm, and combines individual classifiers into ensembles. Robust metrics of classification performance are offered by bootstrap resampling and permutation testing.


News

classyfire NEWS

Release version 0.1-2 January 2014

  • Introducing Unit Tests for automated testing (/tests)
  • New Vignette “classyfire_cheat_sheet” (/vignettes)
  • New function ggFusedHist
  • Message functionality on attach of the package
  • Added thorough checks for input arguments
  • Modified functionality to allow more tests and checks (cfBuild and cfPermute append relevant class)
  • Updated documentation in the help pages (/man)
  • Bug fixes

Reference manual

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install.packages("classyfire")

0.1-2 by Eleni Chatzimichali, 3 years ago


Report a bug at https://github.com/eaHat/classyfire/issues


Browse source code at https://github.com/cran/classyfire


Authors: Eleni Chatzimichali <ea.chatzimichali@gmail.com> and Conrad Bessant <c.bessant@qmul.ac.uk>


Documentation:   PDF Manual  


GPL (>= 2) license


Imports ggplot2, optimbase

Depends on snowfall, e1071, boot, neldermead

Suggests RUnit, knitr


See at CRAN