Combination of interval-valued belief structures based on belief entropy
- URL: http://arxiv.org/abs/2011.13636v1
- Date: Fri, 27 Nov 2020 10:09:52 GMT
- Title: Combination of interval-valued belief structures based on belief entropy
- Authors: Miao Qin, Yongchuan Tang
- Abstract summary: The paper investigates the issues of combination and normalization of interval-valued belief structures within the framework of Dempster-Shafer theory of evidence.
A new optimality approach based on uncertainty measure is developed, where the problem of combining interval-valued belief structures degenerates into combining basic probability assignments.
- Score: 5.221097007424518
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper investigates the issues of combination and normalization of
interval-valued belief structures within the framework of Dempster-Shafer
theory of evidence. Existing approaches are reviewed and thoroughly analyzed.
The advantages and drawbacks of previous approach are presented. A new
optimality approach based on uncertainty measure is developed, where the
problem of combining interval-valued belief structures degenerates into
combining basic probability assignments. Numerical examples are provided to
illustrate the rationality of the proposed approach.
Related papers
- An integrated perspective of robustness in regression through the lens of the bias-variance trade-off [3.0277213703725767]
We examine the relationship between traditional outlier-resistant robust estimation and robust optimization, which focuses on parameter estimation resistant to imaginary dataset-perturbations.
While both are commonly regarded as robust methods, these concepts demonstrate a bias-variance trade-off, indicating that they follow roughly converse strategies.
arXiv Detail & Related papers (2024-07-15T03:47:16Z) - Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations [80.86128012438834]
We show for the first time that computing the robustness of counterfactuals with respect to plausible model shifts is NP-complete.
We propose a novel probabilistic approach which is able to provide tight estimates of robustness with strong guarantees.
arXiv Detail & Related papers (2024-07-10T09:13:11Z) - A Unified Theory of Stochastic Proximal Point Methods without Smoothness [52.30944052987393]
Proximal point methods have attracted considerable interest owing to their numerical stability and robustness against imperfect tuning.
This paper presents a comprehensive analysis of a broad range of variations of the proximal point method (SPPM)
arXiv Detail & Related papers (2024-05-24T21:09:19Z) - Improving Kernel-Based Nonasymptotic Simultaneous Confidence Bands [0.0]
The paper studies the problem of constructing nonparametric simultaneous confidence bands with nonasymptotic and distribition-free guarantees.
The approach is based on the theory of Paley-Wiener kernel reproducing Hilbert spaces.
arXiv Detail & Related papers (2024-01-28T22:43:33Z) - Towards Trustworthy Explanation: On Causal Rationalization [9.48539398357156]
We propose a new model of rationalization based on two causal desiderata, non-spuriousness and efficiency.
The superior performance of the proposed causal rationalization is demonstrated on real-world review and medical datasets.
arXiv Detail & Related papers (2023-06-25T03:34:06Z) - Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
We propose a framework for efficient and effective counterfactual inference implemented with neural networks.
The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data.
Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
arXiv Detail & Related papers (2023-06-09T08:30:51Z) - 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) - On the Minimal Adversarial Perturbation for Deep Neural Networks with
Provable Estimation Error [65.51757376525798]
The existence of adversarial perturbations has opened an interesting research line on provable robustness.
No provable results have been presented to estimate and bound the error committed.
This paper proposes two lightweight strategies to find the minimal adversarial perturbation.
The obtained results show that the proposed strategies approximate the theoretical distance and robustness for samples close to the classification, leading to provable guarantees against any adversarial attacks.
arXiv Detail & Related papers (2022-01-04T16:40:03Z) - Uncertainty-Aware Few-Shot Image Classification [118.72423376789062]
Few-shot image classification learns to recognize new categories from limited labelled data.
We propose Uncertainty-Aware Few-Shot framework for image classification.
arXiv Detail & Related papers (2020-10-09T12:26:27Z) - Causal Modeling with Stochastic Confounders [11.881081802491183]
This work extends causal inference with confounders.
We propose a new approach to variational estimation for causal inference based on a representer theorem with a random input space.
arXiv Detail & Related papers (2020-04-24T00:34:44Z)
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.