COREG: General Interface for COREG model

In SSLR: Semi-Supervised Classification, Regression and Clustering Methods

Description

COREG is a semi-supervised learning for regression with a co-training style. This technique uses two kNN regressors with different distance metrics. For each iteration, each regressor labels the unlabeled example which can be most confidently labeled for the other learner, where the labeling confidence is estimated through considering the consistency of the regressor with the labeled example set. The final prediction is made by averaging the predictions of both the refined kNN regressors

Usage

COREG(max.iter = 50, k1 = 3, k2 = 5, p1 = 3, p2 = 5, u = 100)

Arguments

max.iter	maximum number of iterations to execute the self-labeling process. Default is 50.
k1	parameter in first KNN
k2	parameter in second KNN
p1	distance order 1. Default is 3
p2	distance order 1. Default is 5
u	Number of unlabeled instances in the pool. Default is 100.

Details

labeling data is very expensive computationally. Its so slow. For executing this model, we need RANN installed.

References

Zhi-Hua Zhou and Ming Li.
Semi-Supervised Regression with Co-Training.
National Laboratory for Novel Software Technology Nanjing University, Nanjing 210093, China

Examples

library(SSLR)

m <- COREG(max.iter = 1)

SSLR documentation built on July 22, 2021, 9:08 a.m.