Generic Interface coBCReg model

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.

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