A Robust Semi-supervised Self-training Classification Method
CHEN Mengxing
FAN Yali
HU Yuyu
Abstract：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.
Keywords：semi-supervised classificationself-trainingrobust Mahalanobis distancerandom forestresidual model
Publication Date：2024-11-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 3202-3205,3317 )