In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised Learning

Autor:	Rodemann, Julian, Jansen, Christoph, Schollmeyer, Georg, Augustin, Thomas
Rok vydání:	2023
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Mathematics - Statistics Theory Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	Self-training is a simple yet effective method within semi-supervised learning. The idea is to iteratively enhance training data by adding pseudo-labeled data. Its generalization performance heavily depends on the selection of these pseudo-labeled data (PLS). In this paper, we aim at rendering PLS more robust towards the involved modeling assumptions. To this end, we propose to select pseudo-labeled data that maximize a multi-objective utility function. The latter is constructed to account for different sources of uncertainty, three of which we discuss in more detail: model selection, accumulation of errors and covariate shift. In the absence of second-order information on such uncertainties, we furthermore consider the generic approach of the generalized Bayesian alpha-cut updating rule for credal sets. As a practical proof of concept, we spotlight the application of three of our robust extensions on simulated and real-world data. Results suggest that in particular robustness w.r.t. model choice can lead to substantial accuracy gains. Comment: 9 pages, 1 figure, under review
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.01117 Zobrazit plný text záznamu View this record from Arxiv