Semi-Supervised Self-Training Feature Weighted Clustering Decision Tree and Random Forest

Zhenyu Liu
Zhenyu Liu
Qilong Zhang
Qilong Zhang

IEEE Access, pp. 128337-128348, 2020.

Cited by: 0|Bibtex|Views2|DOI:https://doi.org/10.1109/ACCESS.2020.3008951
Other Links: academic.microsoft.com

Abstract:

A self-training algorithm is an iterative method for semi-supervised learning, which wraps around a base learner. It uses its own predictions to assign labels to unlabeled data. For a self-training algorithm, the classification ability of the base learner and the estimation of prediction confidence are very important. The classical decisi...More

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