mlsanityData to 7 Oct 2026

Generalized Trusted Multi-view Classification Framework with Hierarchical Opinion Aggregation

arXiv 2411.03713 ↗First posted 6 Nov 2024cs.LGcited by 1

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Abstract (arXiv)

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. One can access the source code on https://github.com/lshi91/GTMC-HOA.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 62 references that are in the map. First posted on arXiv 6 Nov 2024.

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19 of 62 references.

References · 19

  1. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 728
  2. 2022Trusted Multi-View Classification with Dynamic Evidential Fusioncited by 167
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7 more
  1. 2023MvFS: Multi-view Feature Selection for Recommender Systemcited by 10
  2. 2022Latent Heterogeneous Graph Network for Incomplete Multi-View Learningcited by 9
  3. 2024Trusted Multi-view Learning under Noisy Supervisioncited by 8
  4. 2023Enhanced Latent Multi-view Subspace Clusteringcited by 6
  5. 2024Nonlinear subspace clustering by functional link neural networkscited by 5
  6. 2023Bridging Trustworthiness and Open-World Learning: An Exploratory Neural Approach for Enhancing Interpretability, Generalization, and Robustnesscited by 4
  7. 2024Dynamic Evidence Decoupling for Trusted Multi-view Learningcited by 4

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  1. 2025Towards Comprehensive Information-theoretic Multi-view Learningcited by 1