Multi-class Probabilistic Bounds for Self-learning

• URL: http://arxiv.org/abs/2109.14422v1
• Date: Wed, 29 Sep 2021 13:57:37 GMT
• Title: Multi-class Probabilistic Bounds for Self-learning
• Authors: Vasilii Feofanov and Emilie Devijver and Massih-Reza Amini
• Abstract summary: Pseudo-labeling is prone to error and runs the risk of adding noisy labels into unlabeled training data. We present a probabilistic framework for analyzing self-learning in the multi-class classification scenario with partially labeled data.
• Score: 13.875239300089861
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-learning is a classical approach for learning with both labeled and unlabeled observations which consists in giving pseudo-labels to unlabeled training instances with a confidence score over a predetermined threshold. At the same time, the pseudo-labeling technique is prone to error and runs the risk of adding noisy labels into unlabeled training data. In this paper, we present a probabilistic framework for analyzing self-learning in the multi-class classification scenario with partially labeled data. First, we derive a transductive bound over the risk of the multi-class majority vote classifier. Based on this result, we propose to automatically choose the threshold for pseudo-labeling that minimizes the transductive bound. Then, we introduce a mislabeling error model to analyze the error of the majority vote classifier in the case of the pseudo-labeled data. We derive a probabilistic C-bound over the majority vote error when an imperfect label is given. Empirical results on different data sets show the effectiveness of our framework compared to several state-of-the-art semi-supervised approaches.

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