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
DOI:	10.32614/CRAN.package.autoBagging
Author:	Fabio Pinto [aut], Vitor Cerqueira [cre], Carlos Soares [ctb], Joao Mendes-Moreira [ctb]
Maintainer:	Vitor Cerqueira <cerqueira.vitormanuel at gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
Citation:	autoBagging citation info
Materials:	README
CRAN checks:	autoBagging results

Documentation:

Reference manual:

autoBagging.pdf

Downloads:

Package source:	autoBagging_0.1.0.tar.gz
Windows binaries:	r-devel: autoBagging_0.1.0.zip, r-release: autoBagging_0.1.0.zip, r-oldrel: autoBagging_0.1.0.zip
macOS binaries:	r-release (arm64): autoBagging_0.1.0.tgz, r-oldrel (arm64): autoBagging_0.1.0.tgz, r-release (x86_64): autoBagging_0.1.0.tgz, r-oldrel (x86_64): autoBagging_0.1.0.tgz

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