Refining Confusion and Ignorance in Trusted Multi-View Classification

Trusted multi-view classification has been widely applied in safety-critical domains by integrating single-view information and providing reliable uncertainty estimates. However, using a unified uncertainty measure overlooks the differences between various types of uncertainty, which impedes the adoption of appropriate learning strategies for different types of uncertain samples to enhance model performance. To address this issue, we propose a novel uncertainty measure based on Dempster-Shafer Theory, which refines uncertainty into confusion caused by multi-view classification conflicts and ignorance arising from unknown classes. Based on the refined uncertainty measure, we implement a trusted multi-view classification algorithm, where the learning objective consists of the loss terms of prediction error, ignorance and confusion. Through optimizing the hybrid learning objective, we can improve the prediction accuracy and identify the unknown and conflicted samples in multi-view classification. Extensive experiments on authorized multi-view datasets validate the superiority of the proposed method. The codes have been released at https://github.com/muxixi727/RTMC.