mfe: Meta-Feature Extractor

Extracts meta-features from datasets to support the design of recommendation systems based on Meta-Learning. The meta-features, also called characterization measures, are able to characterize the complexity of datasets and to provide estimates of algorithm performance. The package contains not only the standard characterization measures, but also more recent characterization measures. By making available a large set of meta-feature extraction functions, tasks like comprehensive data characterization, deep data exploration and large number of Meta-Learning based data analysis can be performed. These concepts are described in the paper: Adriano Rivolli, Luis Garcia, Carlos Soares, Joaquin Vanschoren, and Andre de Carvalho. Towards Reproducible Empirical Research in Meta-Learning.

Version: 0.1.3
Depends: R (≥ 3.3)
Imports: cluster, clusterCrit, e1071, infotheo, MASS, rpart, rrcov, stats, utils
Suggests: knitr, rmarkdown, testthat
Published: 2019-08-26
Author: Adriano Rivolli [aut, cre], Luis P. F. Garcia [aut], Andre C. P. L. F. de Carvalho [ths]
Maintainer: Adriano Rivolli <rivolli at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: mfe results


Reference manual: mfe.pdf
Vignettes: mfe: Meta-Feature Extractor
Package source: mfe_0.1.3.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: mfe_0.1.3.tgz, r-oldrel: mfe_0.1.3.tgz
Old sources: mfe archive


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