autoBagging: Learning to Rank Bagging Workflows with Metalearning

A framework for automated machine learning. Concretely, the focus is on the optimisation of bagging workflows. A bagging workflows is composed by three phases: (i) generation: which and how many predictive models to learn; (ii) pruning: after learning a set of models, the worst ones are cut off from the ensemble; and (iii) integration: how the models are combined for predicting a new observation. autoBagging optimises these processes by combining metalearning and a learning to rank approach to learn from metadata. It automatically ranks 63 bagging workflows by exploiting past performance and dataset characterization. A complete description of the method can be found in: Pinto, F., Cerqueira, V., Soares, C., Mendes-Moreira, J. (2017): "autoBagging: Learning to Rank Bagging Workflows with Metalearning" arXiv preprint arXiv:1706.09367.

Version: 0.1.0
Depends: R (≥ 2.10)
Imports: cluster, xgboost, methods, e1071, rpart, abind, caret, MASS, entropy, lsr, CORElearn, infotheo, minerva, party
Suggests: testthat
Published: 2017-07-02
Author: Fabio Pinto [aut], Vitor Cerqueira [cre], Carlos Soares [ctb], Joao Mendes-Moreira [ctb]
Maintainer: Vitor Cerqueira <cerqueira.vitormanuel at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: autoBagging citation info
Materials: README
CRAN checks: autoBagging results


Reference manual: autoBagging.pdf
Package source: autoBagging_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: autoBagging_0.1.0.tgz
OS X Mavericks binaries: r-oldrel: autoBagging_0.1.0.tgz


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