Three-Way Decision Enhanced Graph Convolutional Networks for Text Classification

The graph convolutional network (GCN) has demonstrated effectiveness well in the text classification task. However, inadequate handling of uncertainty in prediction results exists due to the under-utilization of text features extracted by a single deep-learning model. To mitigate the potential risk of text misclassification, we proposed an enhanced GCN model for text classification based on three-way decision, incorporating shadowed set theory (3WD-GCN). In this approach, we first employ GCN as a primary classifier to handle textual data, obtaining the initial predicted results and the membership matrix. Depending on the idea of processing in threes, these results were divided into acceptance, rejection, and subdivision regions, respectively. For the subdivision region, we introduce SVM as a secondary classifier to process objects with poor conformability and distinguishability, which can reduce the uncertainty of prediction results and improve the overall performance of text classification. A series of experiments based on several benchmark datasets extensively evaluated the proposed method. The results demonstrate the validity of the approach and show a significant improvement over popular baseline text classification models.