semiArtificial: Generator of semi-artificial data

Package semiArtificial contains methods to generate and evaluate semi-artificial data sets. Based on a given data set different methods learn data properties using machine learning algorithms and generate new data with the same properties. The package currently includes the following data generator: -a RBF network based generator using rbfDDA from RSNNS package. More generators are planned in near future. Data evaluation support tools include: -single attribute based statistical evaluation: mean, median,standard deviation, skewness, kurtosis, KS test, Hellinger distance -evaluation based on clustering using Adjusted Rand Index (ARI) -evaluation based on classification performance with various learning models, eg, random forests.

Version: 1.2.0
Imports: RSNNS, CORElearn, MASS, nnet, cluster, mclust, fpc, stats, timeSeries, timeDate
Published: 2014-03-17
Author: Marko Robnik-Sikonja
Maintainer: Marko Robnik-Sikonja <marko.robnik at fri.uni-lj.si>
License: GPL-3
URL: http://lkm.fri.uni-lj.si/rmarko/software/
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: semiArtificial results

Downloads:

Reference manual: semiArtificial.pdf
Package source: semiArtificial_1.2.0.tar.gz
OS X binary: semiArtificial_1.2.0.tgz
Windows binary: semiArtificial_1.2.0.zip
Old sources: semiArtificial archive