A Robust Semi-supervised Self-training Classification Method
The performance of semi-supervised self-training classifier largely depends on the quality of pseudo labels.This paper proposes a robust semi-supervised classification method for logistic regression with l2 regularization,using a random forest training residual model and robust Mahalanobis distance to improve the quality of pseudo-labels.A large number of experiments have been carried out to evaluate the algorithm.
semi-supervised classificationself-trainingrobust Mahalanobis distancerandom forestresidual model
万方数据
维普
