semiArtificial: Generator of Semi-Artificial Data
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.
| Version: |
2.4.1 |
| Imports: |
CORElearn (≥
1.50.3), RSNNS, MASS, nnet, cluster, fpc, stats, timeDate, robustbase, ks, logspline, methods, mcclust, flexclust, StatMatch |
| Published: |
2021-09-23 |
| DOI: |
10.32614/CRAN.package.semiArtificial |
| 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 |
Documentation:
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Reverse dependencies:
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