A Novel Method for Pignistic Information Fusion in the View of Z-number

• URL: http://arxiv.org/abs/2501.06201v1
• Date: Fri, 27 Dec 2024 18:17:28 GMT
• Title: A Novel Method for Pignistic Information Fusion in the View of Z-number
• Authors: Yuanpeng He,
• Abstract summary: Dempster-Shafer evidences theory (DSET) is widely used to handle uncertain information. Based on DSET, a completely new method to fuse information from different sources based on pignistic transformation and Z-numbers is proposed in this paper.
• Score: 0.0
• License:
• Abstract: How to properly fuse information from complex sources is still an open problem. Lots of methods have been put forward to provide a effective solution in fusing intricate information. Among them, Dempster-Shafer evidences theory (DSET) is one of the representatives, it is widely used to handle uncertain information. Based on DSET, a completely new method to fuse information from different sources based on pignistic transformation and Z-numbers is proposed in this paper which is able to handle separate situations of information and keeps high accuracy in producing rational and correct judgments on actual situations. Besides, in order to illustrate the superiority of the proposed method, some numerical examples and application are also provided to verify the validity and robustness of it.

Related papers

• A novel framework for MCDM based on Z numbers and soft likelihood function [0.0]
  This paper devises a novel framework of soft likelihood function based on information volume of fuzzy membership and credibility measure. An application is provided to verify the validity and correctness of the proposed framework.
  arXiv  Detail & Related papers  (2024-12-26T18:47:19Z)
• What If the Input is Expanded in OOD Detection? [77.37433624869857]
  Out-of-distribution (OOD) detection aims to identify OOD inputs from unknown classes. Various scoring functions are proposed to distinguish it from in-distribution (ID) data. We introduce a novel perspective, i.e., employing different common corruptions on the input space.
  arXiv  Detail & Related papers  (2024-10-24T06:47:28Z)
• FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis Assistant [59.2438504610849]
  We introduce FFAA: Face Forgery Analysis Assistant, consisting of a fine-tuned Multimodal Large Language Model (MLLM) and Multi-answer Intelligent Decision System (MIDS) Our method not only provides user-friendly and explainable results but also significantly boosts accuracy and robustness compared to previous methods.
  arXiv  Detail & Related papers  (2024-08-19T15:15:20Z)
• Entropy-Based Decoding for Retrieval-Augmented Large Language Models [43.93281157539377]
  Augmenting Large Language Models with retrieved external knowledge has proven effective for improving the factual accuracy of generated responses. We introduce a novel, training-free decoding method guided by entropy considerations to mitigate this issue.
  arXiv  Detail & Related papers  (2024-06-25T12:59:38Z)
• Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation [13.134162427636356]
  Mis- and disinformation online have become a major societal problem. One common form of mis- and disinformation is out-of-context (OOC) information. We propose a stance extraction network (SEN) that can extract the stances of different pieces of multi-modal evidence.
  arXiv  Detail & Related papers  (2023-11-03T08:05:54Z)
• Information Fusion for Assistance Systems in Production Assessment [49.40442046458756]
  We provide a framework for the fusion of n number of information sources using the evidence theory. We provide a methodology for the information fusion of two primary sources: an ensemble classifier based on machine data and an expert-centered model. We address the problem of data drift by proposing a methodology to update the data-based models using an evidence theory approach.
  arXiv  Detail & Related papers  (2023-08-31T22:08:01Z)
• Manually Selecting The Data Function for Supervised Learning of small datasets [0.0]
  Supervised learning problems may become ill-posed when there is a lack of information. Initializing an informative ill-posed operator is akin to posing better questions to achieve more accurate answers. The Fredholm integral equation of the first kind (FIFK) is a reliable ill-posed operator that can integrate distributions and prior knowledge as input information.
  arXiv  Detail & Related papers  (2023-03-07T13:38:04Z)
• MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization [55.60306377044225]
  State-of-the-art summarization systems can generate highly fluent summaries. These summaries, however, may contain factual inconsistencies and/or information not present in the source. We introduce an alternative scheme based on standard information-theoretic measures in which the information present in the source and summary is directly compared.
  arXiv  Detail & Related papers  (2023-01-28T23:08:25Z)
• Principled Knowledge Extrapolation with GANs [92.62635018136476]
  We study counterfactual synthesis from a new perspective of knowledge extrapolation. We show that an adversarial game with a closed-form discriminator can be used to address the knowledge extrapolation problem. Our method enjoys both elegant theoretical guarantees and superior performance in many scenarios.
  arXiv  Detail & Related papers  (2022-05-21T08:39:42Z)
• A Bayesian Framework for Information-Theoretic Probing [51.98576673620385]
  We argue that probing should be seen as approximating a mutual information. This led to the rather unintuitive conclusion that representations encode exactly the same information about a target task as the original sentences. This paper proposes a new framework to measure what we term Bayesian mutual information.
  arXiv  Detail & Related papers  (2021-09-08T18:08:36Z)
• A new approach for generation of generalized basic probability assignment in the evidence theory [5.794599007795347]
  Dempster-Shafer evidence theory is widely used in multi-source information fusion. This paper studies the generation of basic probability assignment (BPA) with incomplete information. The proposed method can not only be used in different complex environments simply and flexibly, but also have less information loss in information processing.
  arXiv  Detail & Related papers  (2020-04-06T15:40:35Z)

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.