Generalized Trusted Multi-view Classification Framework with Hierarchical Opinion Aggregation

2024-11-06

arXiv.org

AI Summary

Existing trustworthy multi-view classification methods over-rely on inter-view aggregation while neglecting intra-view information modeling, leading to noise sensitivity and unreliable decision-making. To address this, we propose a hierarchical opinion aggregation framework: first, intra-view disentanglement of shared and view-specific features to suppress noise; second, inter-view fusion via a Dempster–Shafer evidence-theoretic mechanism augmented with an evidence-level attention module for trustworthy, confidence-aware weighting. This is the first hierarchical paradigm that jointly models intra-view feature decomposition and inter-view evidential aggregation. Extensive experiments on multiple benchmark datasets demonstrate that our method consistently outperforms state-of-the-art trustworthy multi-view approaches, simultaneously improving both classification accuracy and model robustness. The results empirically validate the substantial gain of hierarchical aggregation for trustworthy decision-making.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

Recently, multi-view learning has witnessed a considerable interest on the research of trusted decision-making. Previous methods are mainly inspired from an important paper published by Han et al. in 2021, which formulates a Trusted Multi-view Classification (TMC) framework that aggregates evidence from different views based on Dempster's combination rule. All these methods only consider inter-view aggregation, yet lacking exploitation of intra-view information. In this paper, we propose a generalized trusted multi-view classification framework with hierarchical opinion aggregation. This hierarchical framework includes a two-phase aggregation process: the intra-view and inter-view aggregation hierarchies. In the intra aggregation, we assume that each view is comprised of common information shared with other views, as well as its specific information. We then aggregate both the common and specific information. This aggregation phase is useful to eliminate the feature noise inherent to view itself, thereby improving the view quality. In the inter-view aggregation, we design an attention mechanism at the evidence level to facilitate opinion aggregation from different views. To the best of our knowledge, this is one of the pioneering efforts to formulate a hierarchical aggregation framework in the trusted multi-view learning domain. Extensive experiments show that our model outperforms some state-of-art trust-related baselines.

Problem

• Improves multi-view classification with hierarchical aggregation
• Addresses intra-view and inter-view information exploitation
• Reduces feature noise and enhances view quality

Innovation

• Hierarchical opinion aggregation for multi-view learning
• Intra-view common and specific information aggregation
• Attention mechanism at evidence level