| coBCReg {SSLR} | R Documentation |
General 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
coBCReg(learner, N = 3, perc.full = 0.7, u = 100, max.iter = 50)
Arguments
learner |
model from parsnip package for training a supervised base classifier using a set of instances. This model need to have probability predictions |
N |
The number of classifiers used as committee members. All these classifiers
are trained using the |
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. |
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