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classyfire (version 0.1-2)

Robust multivariate classification using highly optimised SVM ensembles

Description

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.

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Version

Install

install.packages('classyfire')

Monthly Downloads

14

Version

0.1-2

License

GPL (>= 2)

Maintainer

Eleni Chatzimichali

Last Published

January 12th, 2015

Functions in classyfire (0.1-2)

getAcc

Get the accuracies of a classification ensemble
classyfire-package

Robust multivariate classification using highly optimised SVM ensembles
ggEnsTrend

Trend of the test accuracies
getConfMatr

Confusion matrix summarising the performance of an ensemble
getOptParam

Get the optimal SVM hyperparameters of a classification ensemble
cfPredict

Predict the class of new data using an existing ensemble
cfBuild

Create a highly optimised ensemble of RBF SVM classifiers
cfPermute

Permutation testing to indicate statistical significance of performance
ggClassPred

Barplot of the per class accuracies.
getAvgAcc

Get the average accuracies of a classification ensemble
ggFusedHist

Fused histograms of ensemble and permutation results
getPerm5Num

Get descriptive statistics from a permutation object
ggEnsHist

Ensemble Histograms
ggPermHist

Permutation Histograms