Package: autoBagging
Type: Package
Title: Learning to Rank Bagging Workflows with Metalearning
Version: 0.1.0
Authors@R: c(person("Fabio", "Pinto", role = c("aut")),
              person("Vitor", "Cerqueira", email = "cerqueira.vitormanuel@gmail.com", role = "cre"),
              person("Carlos", "Soares", role = "ctb"),
              person("Joao", "Mendes-Moreira", role = "ctb"))
Author: Fabio Pinto [aut],
        Vitor Cerqueira [cre],
        Carlos Soares [ctb],
        Joao Mendes-Moreira [ctb]
Maintainer: Vitor Cerqueira <cerqueira.vitormanuel@gmail.com>
Description: 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.
Depends: R (>= 2.10)
Imports: cluster, xgboost, methods, e1071, rpart, abind, caret, MASS,
        entropy, lsr, CORElearn, infotheo, minerva, party
License: GPL (>= 2)
Encoding: UTF-8
LazyData: no
RoxygenNote: 6.0.1
Suggests: testthat
NeedsCompilation: no
Packaged: 2017-07-01 16:56:00 UTC; root
Repository: CRAN
Date/Publication: 2017-07-02 00:06:44 UTC
Built: R 3.5.3; ; 2020-04-22 08:58:28 UTC; windows
