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

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

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...

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Bibliographic Details
Main Authors: Zhenyu Liu, Tao Wen, Wei Sun, Qilong Zhang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Semi-supervised learning
self-training
decision tree
random forest
node splits
Online Access:https://ieeexplore.ieee.org/document/9139499/

Internet

https://ieeexplore.ieee.org/document/9139499/

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