Semi-supervised text classification method based on three-way decision with evidence theory

Abstract

Semi-supervised learning methods play a crucial role in text classification tasks. However, due to limitation of scarce labeled training data, the uncertainty of pseudo labels is still an unavoidable problem in semi-supervised text classification. To address this issue, this paper introduces three-way decision theory into semi-supervised text classification model, which divides the model output pseudo-labeled samples into different regions and adopts different processing strategies. The accurate and effective pseudo-labeled samples are selected as much as possible to expand the original training set. For the pseudo-labeled outputs by the model, we use evidence theory to fuse the probability outputs of the samples to improve the stability and credibility of pseudo labels. Experimental results demonstrate that the method introduced in this paper effectively enhances the accuracy of semi-supervised text classification while exhibiting high stability.