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,
-a Random Forest based generator for both classification and regression problems
-a density forest based generator for unsupervised data
Data evaluation support tools include:
-single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance
-evaluation based on clustering using Adjusted Rand Index (ARI) and FM
-evaluation based on classification performance with various learning models, eg, random forests.
||CORElearn, RSNNS, MASS, nnet, cluster, mclust, fpc, stats, timeDate, robustbase, dendextend, ks, logspline, methods
||Marko Robnik-Sikonja <marko.robnik at fri.uni-lj.si>
Please use the canonical form
to link to this page.