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: Rivolli A., Garcia L., Soares c., Vanschoren J. and Carvalho A. (2018) <arXiv:1808.10406>.
| Version: | 0.1.5 |
| Depends: | R (≥ 3.3) |
| Imports: | cluster, clusterCrit, ECoL (≥ 0.3), e1071, infotheo, MASS, rpart, rrcov, stats, utils |
| Suggests: | knitr, rmarkdown, testthat |
| Published: | 2020-05-05 |
| Author: | Adriano Rivolli [aut, cre], Luis P. F. Garcia [aut], Andre C. P. L. F. de Carvalho [ths] |
| Maintainer: | Adriano Rivolli <rivolli at utfpr.edu.br> |
| BugReports: | https://github.com/rivolli/mfe/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/rivolli/mfe |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | mfe results |
| Reference manual: | mfe.pdf |
| Vignettes: |
mfe: Meta-Feature Extractor |
| Package source: | mfe_0.1.5.tar.gz |
| Windows binaries: | r-devel: mfe_0.1.5.zip, r-release: mfe_0.1.5.zip, r-oldrel: mfe_0.1.5.zip |
| macOS binaries: | r-release: mfe_0.1.5.tgz, r-oldrel: mfe_0.1.5.tgz |
| Old sources: | mfe archive |
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