  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.
