Reinforced confidence in self-training for a semi-supervised medical data classification

Abstract

The implication of semi-supervised method has become crucial to automate tasks that require the manual human expertise for data labelling, the advantage of this method resides in the fact that they require a low amount of labelled information. In this work, we are particularly interested in self-training paradigm. These techniques use the same principle as those of supervised techniques, but with a confidence measure that allows only a selection of the most confident samples. We propose a novel self-training algorithm named reinforced confidence in self-training (R-COSET) based on an iterative process. In each iteration the learned hypothesis can be improved by confidence data, where the proposed confidence measure is reinforced by two confidence levels in order to increase the robustness of the self-training process. Experiments show that the introduction of the second level of the neighbourhood graph in confidence measure is beneficial and that R-COSET can effectively improve classification performance.