Dynamic Evidence Decoupling for Trusted Multi-view Learning

TL;DR

This paper introduces CCML, a novel multi-view learning method that dynamically decouples consistent and complementary evidence to improve decision reliability in safety-critical applications.

Contribution

It proposes a dynamic evidence decoupling strategy using evidential neural networks to better handle semantic vagueness in multi-view data, enhancing trustworthiness.

Findings

CCML outperforms state-of-the-art baselines in accuracy and reliability.

Dynamic evidence decoupling effectively addresses semantic vagueness.

The method is validated on synthetic and real-world datasets.

Abstract

Multi-view learning methods often focus on improving decision accuracy, while neglecting the decision uncertainty, limiting their suitability for safety-critical applications. To mitigate this, researchers propose trusted multi-view learning methods that estimate classification probabilities and uncertainty by learning the class distributions for each instance. However, these methods assume that the data from each view can effectively differentiate all categories, ignoring the semantic vagueness phenomenon in real-world multi-view data. Our findings demonstrate that this phenomenon significantly suppresses the learning of view-specific evidence in existing methods. We propose a Consistent and Complementary-aware trusted Multi-view Learning (CCML) method to solve this problem. We first construct view opinions using evidential deep neural networks, which consist of belief mass vectors and uncertainty estimates. Next, we dynamically decouple the consistent and complementary evidence. The consistent evidence is derived from the shared portions across all views, while the complementary evidence is obtained by averaging the differing portions across all views. We ensure that the opinion constructed from the consistent evidence strictly aligns with the ground-truth category. For the opinion constructed from the complementary evidence, we allow it for potential vagueness in the evidence. We compare CCML with state-of-the-art baselines on one synthetic and six real-world datasets. The results validate the effectiveness of the dynamic evidence decoupling strategy and show that CCML significantly outperforms baselines on accuracy and reliability. The code is released at https://github.com/Lihong-Liu/CCML.