Resolving conflicts is essential to make the decisions of multi-view classification more reliable. Much research has been conducted on learning consistent informative representations among different views, assuming that all views are identically important and strictly aligned. However, real-world multi-view data may not always conform to these assumptions, as some views may express distinct information. To address this issue, we develop a computational trust-based discounting method to enhance the existing trustworthy framework in scenarios where conflicts between different views may arise. Its belief fusion process considers the trustworthiness of predictions made by individual views via an instance-wise probability-sensitive trust discounting mechanism. We evaluate our method on six real-world datasets, using Top-1 Accuracy, AUC-ROC for Uncertainty-Aware Prediction, Fleiss' Kappa, and a new metric called Multi-View Agreement with Ground Truth that takes into consideration the ground truth labels. The experimental results show that computational trust can effectively resolve conflicts, paving the way for more reliable multi-view classification models in real-world applications.

解决冲突是使多视角分类决策更可靠的关键，现有的基于信任的框架无法适应不同视角之间可能出现的冲突，因此我们开发了一种计算信任的折扣方法来增强现有的框架，通过考虑实例级概率敏感的信任折扣机制，对个体视角的预测进行融合以提高可靠性，在真实应用中的六个数据集上验证了我们的方法，使用了Top-1准确率、AUC-ROC用于不确定性感知预测、Fleiss' Kappa以及考虑了基准真实标签的新指标Multi-View一致性，实验结果表明，计算机信任可以有效解决冲突，为更可靠的多视角分类模型铺平了道路。

导航冲突观点：利用信任学习