arXiv Machine Learning
Negation-Aware Weighted Information Fusion for Reliable Evidential Reasoning
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Aug 14
Robust Dempster-Shafer Evidence Fusion with Chaos-Conflict Measurement and Historical-Experience Weighting
arXiv:2608. 13108v1 Announce Type: new Abstract: Multi-source evidence fusion under Dempster-Shafer theory faces two persistent challenges: existing conflict measures assess inter-evidence inconsistency and intra-evidence uncertainty independently, yielding incomplete evaluations, and current fusion methods evaluate evidence sources exclusively through instantaneous comparisns without exploiting their long-term reliability across diverse decision contexts.
arXiv Machine Learning
Aug 4
An Evidence Hierarchy for Bayesian Object Classification via OSINT-Aided Heterogeneous Sensor Fusion
arXiv:2605. 22259v2 Announce Type: replace Abstract: Heterogeneous sensor fusion is vital for detecting, localizing, and classifying CBRNE threats.
arXiv Machine Learning
Aug 10
Conformal Fusion Under Missing Modalities
arXiv:2608. 07183v1 Announce Type: new Abstract: Multimodal fusion architectures typically assume all modalities are available at inference, yet sensor failures, acquisition variability, and cost constraints routinely produce incomplete observations.
arXiv AI
Sep 10
Three Types of Negation of Triple and its Elements and an Extension of Triple
arXiv:2609.08271v1 Announce Type: new Abstract: In various data models, the classical triple is a typical semantic data model. However, due to the design of the triple as a simple structure for repre...
arXiv AI
Jun 29
Single and Multi Truth Data Fusion using Large Language Models
arXiv:2606. 28062v1 Announce Type: cross Abstract: Data fusion, also known as truth discovery, is a data integration problem that aims to determine the correct value or set of values for each attribute of an object when presented with potentially conflicting values from multiple sources.
arXiv Machine Learning
Sep 14
Are Independently Estimated View Uncertainties Comparable? Unified Routing for Trusted Multi-View Classification
The paper introduces Trusted Multi-view learning with Unified Routing (TMUR), a method that separates view-specific evidence extraction from fusion arbitration in multi-view classification. TMUR employs view-private experts, a collaborative expert, and a unified router that assigns sample-level weights based on global context, along with soft load-balancing and diversity regularization to promote balanced and discriminative expert use. Experiments on 14 datasets show that TMUR consistently improves classification accuracy and reliability compared to 15 recent baselines.