Navigating Conflicting Views: Harnessing Trust for Learning

• URL: http://arxiv.org/abs/2406.00958v1
• Date: Mon, 3 Jun 2024 03:22:18 GMT
• Title: Navigating Conflicting Views: Harnessing Trust for Learning
• Authors: Jueqing Lu, Lan Du, Wray Buntine, Myong Chol Jung, Joanna Dipnall, Belinda Gabbe,
• Abstract summary: We develop a computational trust-based discounting method to enhance the existing trustworthy framework. 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.
• Score: 5.4486293124577125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Dynamic Evidence Decoupling for Trusted Multi-view Learning [17.029245880233816]
  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. The results validate the effectiveness of the dynamic evidence decoupling strategy and show that CCML significantly outperforms baselines on accuracy and reliability.
  arXiv  Detail & Related papers  (2024-10-04T03:27:51Z)
• Evidential Deep Partial Multi-View Classification With Discount Fusion [24.139495744683128]
  We propose a novel framework called Evidential Deep Partial Multi-View Classification (EDP-MVC) We use K-means imputation to address missing views, creating a complete set of multi-view data. The potential conflicts and uncertainties within this imputed data can affect the reliability of downstream inferences.
  arXiv  Detail & Related papers  (2024-08-23T14:50:49Z)
• Regularized Contrastive Partial Multi-view Outlier Detection [76.77036536484114]
  We propose a novel method named Regularized Contrastive Partial Multi-view Outlier Detection (RCPMOD) In this framework, we utilize contrastive learning to learn view-consistent information and distinguish outliers by the degree of consistency. Experimental results on four benchmark datasets demonstrate that our proposed approach could outperform state-of-the-art competitors.
  arXiv  Detail & Related papers  (2024-08-02T14:34:27Z)
• ELFNet: Evidential Local-global Fusion for Stereo Matching [17.675146012208124]
  We introduce the textbfEvidential textbfLocal-global textbfFusion (ELF) framework for stereo matching. It endows both uncertainty estimation and confidence-aware fusion with trustworthy heads.
  arXiv  Detail & Related papers  (2023-08-01T15:51:04Z)
• Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity [61.05259660910437]
  We propose a global consistency and complementarity network (CoCoNet) to learn representations from multiple views. On the global stage, we reckon that the crucial knowledge is implicitly shared among views, and enhancing the encoder to capture such knowledge can improve the discriminability of the learned representations. Lastly on the local stage, we propose a complementarity-factor, which joints cross-view discriminative knowledge, and it guides the encoders to learn not only view-wise discriminability but also cross-view complementary information.
  arXiv  Detail & Related papers  (2022-09-16T09:24:00Z)
• Variational Distillation for Multi-View Learning [104.17551354374821]
  We design several variational information bottlenecks to exploit two key characteristics for multi-view representation learning. Under rigorously theoretical guarantee, our approach enables IB to grasp the intrinsic correlation between observations and semantic labels.
  arXiv  Detail & Related papers  (2022-06-20T03:09:46Z)
• Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization [73.89239820192894]
  We argue that automated counterfactual generation should regard several aspects of the produced adversarial instances. We present a novel framework for the generation of counterfactual examples.
  arXiv  Detail & Related papers  (2022-05-20T15:02:53Z)
• Trusted Multi-View Classification with Dynamic Evidential Fusion [73.35990456162745]
  We propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC) TMC provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. Both theoretical and experimental results validate the effectiveness of the proposed model in accuracy, robustness and trustworthiness.
  arXiv  Detail & Related papers  (2022-04-25T03:48:49Z)
• Trusted Multi-View Classification [76.73585034192894]
  We propose a novel multi-view classification method, termed trusted multi-view classification. It provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. The proposed algorithm jointly utilizes multiple views to promote both classification reliability and robustness.
  arXiv  Detail & Related papers  (2021-02-03T13:30:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.