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.

Automatic Hyperparameter Optimization of Bagging Workflows

Authored by: Fábio Pinto and Vítor Cerqueira

autoBagging is an R package for automatically optimizing bagging workflows to solve classification predictive tasks.


Currently, autoBagging is only available in Github. Soon it will be submitted to CRAN.

Install the package using devtools:

  • devtools::install_github("hadley/devtools")

followed by:

  • devtools::install_github("fhpinto/autoBagging")

In some OS, the installation might need manual installation of recursive dependencies (e.g. data.table).


The core function is autoBagging. Its input is simply the formula for the predictive classification task and the dataset:

  • auto_model <- autoBagging(formula, train.data)

For predicting new instances, the model uses the standard predict method:

  • preds <- predict(auto_model, test.data)

Contact us at: {fhpinto, vmac}@inesctec.pt


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.1.0 by Vitor Cerqueira, 4 years ago

Browse source code at https://github.com/cran/autoBagging

Authors: Fabio Pinto [aut] , Vitor Cerqueira [cre] , Carlos Soares [ctb] , Joao Mendes-Moreira [ctb]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports cluster, xgboost, methods, e1071, rpart, abind, caret, MASS, entropy, lsr, CORElearn, infotheo, minerva, party

Suggests testthat

See at CRAN