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semiArtificial
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Download and install semiArtificial package within the R console
Install from CRAN:
install.packages("semiArtificial")
Install from Github:
library("remotes")
install_github("cran/semiArtificial")
Install by package version:
library("remotes")
install_version("semiArtificial", "2.4.1")
Attach the package and use:
library("semiArtificial")
Maintained by
Marko Robnik-Sikonja
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
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First Published: 2014-02-26
Latest Update: 2021-09-23
Description:
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 generators:
i) a RBF network based generator using rbfDDA() from package 'RSNNS',
ii) a Random Forest based generator for both classification and regression problems
iii) a density forest based generator for unsupervised data
Data evaluation support tools include:
a) single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance
b) evaluation based on clustering using Adjusted Rand Index (ARI) and FM
c) evaluation based on classification performance with various learning models, e.g., random forests.
How to cite:
Marko Robnik-Sikonja (2014). semiArtificial: Generator of Semi-Artificial Data. R package version 2.4.1, https://cran.r-project.org/web/packages/semiArtificial
Previous versions and publish date:
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Some associated functions: cleanData . dataSimilarity . dsClustCompare . newdata . performanceCompare . rbfDataGen . semiArtificial-package . treeEnsemble .
Some associated R codes: dataQuality.R . densityTree.R . forestDataGen.R . init.R . misc.R . rbfDataGen.R . rndtrees.R . Full semiArtificial package functions and examples
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