SSLR: coBCRegG – R documentation

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coBCRegG

Generic Interface coBCReg model

Description

coBCReg is based on an ensemble of N diverse regressors. At each iteration and for each regressor, the companion committee labels the unlabeled examples then the regressor select the most informative newly-labeled examples for itself, where the selection confidence is based on estimating the validation error. The final prediction is the average of the estimates of the N regressors.

Usage

coBCRegG(
  y,
  gen.learner,
  gen.pred,
  N = 3,
  perc.full = 0.7,
  u = 100,
  max.iter = 50,
  gr = 1
)

Arguments

`y`	A vector with the labels of training instances. In this vector the unlabeled instances are specified with the value `NA`.
`gen.learner`	A function for training `N` supervised base classifiers. This function needs two parameters, indexes and cls, where indexes indicates the instances to use and cls specifies the classes of those instances.
`gen.pred`	A function for predicting the probabilities per classes. This function must be two parameters, model and indexes, where the model is a classifier trained with `gen.learner` function and indexes indicates the instances to predict.
`N`	The number of classifiers used as committee members. All these classifiers are trained using the `gen.learner` function. Default is 3.
`perc.full`	A number between 0 and 1. If the percentage of new labeled examples reaches this value the self-labeling process is stopped. Default is 0.7.
`u`	Number of unlabeled instances in the pool. Default is 100.
`max.iter`	Maximum number of iterations to execute in the self-labeling process. Default is 50.
`gr`	growing rate

Details

For regression tasks, labeling data is very expensive computationally. Its so slow.

References

Mohamed Farouk Abdel-Hady, Mohamed Farouk Abdel-Hady and Günther Palm.
Semi-supervised Learning for Regression with Cotraining by Committee
Institute of Neural Information Processing University of Ulm D-89069 Ulm, Germany

SSLR

Semi-Supervised Classification, Regression and Clustering Methods

v0.9.3.1

GPL-3

Authors

Francisco Jesús Palomares Alabarce [aut, cre] (<https://orcid.org/0000-0002-0499-7034>), José Manuel Benítez [ctb] (<https://orcid.org/0000-0002-2346-0793>), Isaac Triguero [ctb] (<https://orcid.org/0000-0002-0150-0651>), Christoph Bergmeir [ctb] (<https://orcid.org/0000-0002-3665-9021>), Mabel González [ctb] (<https://orcid.org/0000-0003-0152-444X>)

Initial release