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.


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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0.1-2 by Eleni Chatzimichali, 5 years ago

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Authors: Eleni Chatzimichali <[email protected]> and Conrad Bessant <[email protected]>

Documentation:   PDF Manual  

GPL (>= 2) license

Imports ggplot2, optimbase

Depends on snowfall, e1071, boot, neldermead

Suggests RUnit, knitr

See at CRAN