Trusted Multi-view Learning for Long-tailed Classification

Class imbalance has been extensively studied in single-view scenarios; however, addressing this challenge in multi-view contexts remains an open problem, with even scarcer research focusing on trustworthy solutions. In this paper, we tackle a particularly challenging class imbalance problem in multi-view scenarios: long-tailed classification. We propose TMLC, a Trusted Multi-view Long-tailed Classification framework, which makes contributions on two critical aspects: opinion aggregation and pseudo-data generation. Specifically, inspired by Social Identity Theory, we design a group consensus opinion aggregation mechanism that guides decision-making toward the direction favored by the majority of the group. In terms of pseudo-data generation, we introduce a novel distance metric to adapt SMOTE for multi-view scenarios and develop an uncertainty-guided data generation module that produces high-quality pseudo-data, effectively mitigating the adverse effects of class imbalance. Extensive experiments on long-tailed multi-view datasets demonstrate that our model is capable of achieving superior performance.

Authors

• Chuanqing Tang Southwestern University of Finance and Economics Artificial Intelligence and Digital Finance Key Laboratory of Sichuan Province
• Yifei Shi National University of Defense Technology
• Guanghao Lin Southwestern University of Finance and Economics
• Lei Xing Xi'an Jiaotong University
• Long Shi

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