Foundational theories of hesitant fuzzy sets and families of hesitant fuzzy sets
- URL: http://arxiv.org/abs/2311.04256v4
- Date: Fri, 18 Apr 2025 01:10:32 GMT
- Title: Foundational theories of hesitant fuzzy sets and families of hesitant fuzzy sets
- Authors: Shizhan Lu, Zeshui Xu, Zhu Fu, Longsheng Cheng, Tongbin Yang,
- Abstract summary: Hesitant fuzzy sets find extensive application in specific scenarios involving uncertainty and hesitation.<n>As a type of sets, hesitant fuzzy sets necessitate a clear and explicit definition of the inclusion relationship.
- Score: 24.890815871172745
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Hesitant fuzzy sets find extensive application in specific scenarios involving uncertainty and hesitation. In the context of set theory, the concept of inclusion relationship holds significant importance as a fundamental definition. Consequently, as a type of sets, hesitant fuzzy sets necessitate a clear and explicit definition of the inclusion relationship. Based on the discrete form of hesitant fuzzy membership degrees, this study proposes multiple types of inclusion relationships for hesitant fuzzy sets. Subsequently, this paper introduces foundational propositions related to hesitant fuzzy sets, as well as propositions concerning families of hesitant fuzzy sets.
Related papers
- An order-oriented approach to scoring hesitant fuzzy elements [4.991981804514581]
This paper proposes a unified framework, where each score is explicitly defined with respect to a given order.<n>We introduce a class of functions, called dominance functions, for ranking hesitant fuzzy elements.<n>We show that these can be employed to construct fuzzy preference relations on typical hesitant fuzzy sets and support group decision-making.
arXiv Detail & Related papers (2026-02-18T19:48:37Z) - Unifying Information-Theoretic and Pair-Counting Clustering Similarity [51.660331450043806]
Clustering similarity measures are typically organized into two principal families, pair-counting and information-theoretic.<n>Here, we develop an analytical framework that unifies these families through two complementary perspectives.
arXiv Detail & Related papers (2025-11-04T21:13:32Z) - FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion [36.714348668366]
We propose a sound and efficient formulation of set representation learning based on its volume approximation as a fuzzy set.<n>The resulting embedding framework, Fuzzy Set Embedding (FUSE), satisfies all set operations and compactly approximates the underlying fuzzy set.
arXiv Detail & Related papers (2025-06-10T03:28:32Z) - Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond [0.0]
fuzzy sets, neutrosophic sets, rough sets, soft sets, and soft sets have been introduced.
neutrosophic sets, which simultaneously represent truth, indeterminacy, and falsehood, have proven to be valuable tools for modeling uncertainty in complex systems.
This paper explores new graph and set concepts, as well as hyper and superhyper concepts.
arXiv Detail & Related papers (2024-11-24T04:28:53Z) - A Universal Sets-level Optimization Framework for Next Set Recommendation [15.808908615022709]
Next Set Recommendation (NSRec) stands as a trending research topic.
We unveil a universal and S ets-level optimization framework for N ext Set Recommendation (SNSRec)
Our approach consistently outperforms previous methods on both relevance and diversity.
arXiv Detail & Related papers (2024-10-30T13:53:46Z) - Foundational propositions of hesitant fuzzy soft $\beta$-covering
approximation spaces [0.0]
Hesitant fuzzy sets exhibit diverse membership degrees, giving rise to various forms of inclusion relationships among them.
This article introduces the notions of hesitant fuzzy soft $beta$-coverings and hesitant fuzzy soft $beta$-neighborhoods.
arXiv Detail & Related papers (2024-03-08T13:16:17Z) - Enhancing Neural Subset Selection: Integrating Background Information into Set Representations [53.15923939406772]
We show that when the target value is conditioned on both the input set and subset, it is essential to incorporate an textitinvariant sufficient statistic of the superset into the subset of interest.
This ensures that the output value remains invariant to permutations of the subset and its corresponding superset, enabling identification of the specific superset from which the subset originated.
arXiv Detail & Related papers (2024-02-05T16:09:35Z) - Likelihood Ratio Confidence Sets for Sequential Decision Making [51.66638486226482]
We revisit the likelihood-based inference principle and propose to use likelihood ratios to construct valid confidence sequences.
Our method is especially suitable for problems with well-specified likelihoods.
We show how to provably choose the best sequence of estimators and shed light on connections to online convex optimization.
arXiv Detail & Related papers (2023-11-08T00:10:21Z) - Ensemble of Counterfactual Explainers [17.88531216690148]
We propose an ensemble of counterfactual explainers that boosts weak explainers, which provide only a subset of such properties.
The ensemble runs weak explainers on a sample of instances and of features, and it combines their results by exploiting a diversity-driven selection function.
arXiv Detail & Related papers (2023-08-29T10:21:50Z) - On Computing Probabilistic Abductive Explanations [30.325691263226968]
The most widely studied explainable AI (XAI) approaches are unsound.
PI-explanations also exhibit important drawbacks, the most visible of which is arguably their size.
This paper investigates practical approaches for computing relevant sets for a number of widely used classifiers.
arXiv Detail & Related papers (2022-12-12T15:47:10Z) - Fuzziness, Indeterminacy and Soft Sets: Frontiers and Perspectives [0.0]
The paper comes across the main steps that laid from Zadeh's fuzziness ana Atanassov's intuitionistic fuzzy sets to Smarandache's indeterminacy and to Molodstov's soft sets.
It is described how the concept of topological space can be extended to fuzzy structures and how to generalize the fundamental mathematical concepts of limit, continuity compactness and Hausdorff space within such kind of structures.
arXiv Detail & Related papers (2022-11-10T07:09:07Z) - Revisiting initial sets in abstract argumentation [7.249126423531563]
We revisit the notion of initial sets by Xu and Cayrol, i.e., non-empty minimal admissible sets in argumentation frameworks.
We contribute with new insights on the structure of initial sets and devise a simple non-deterministic construction principle for any admissible set.
arXiv Detail & Related papers (2022-04-21T09:23:12Z) - A Variational Inference Approach to Inverse Problems with Gamma
Hyperpriors [60.489902135153415]
This paper introduces a variational iterative alternating scheme for hierarchical inverse problems with gamma hyperpriors.
The proposed variational inference approach yields accurate reconstruction, provides meaningful uncertainty quantification, and is easy to implement.
arXiv Detail & Related papers (2021-11-26T06:33:29Z) - Exploring Set Similarity for Dense Self-supervised Representation
Learning [96.35286140203407]
We propose to explore textbfset textbfsimilarity (SetSim) for dense self-supervised representation learning.
We generalize pixel-wise similarity learning to set-wise one to improve the robustness because sets contain more semantic and structure information.
Specifically, by resorting to attentional features of views, we establish corresponding sets, thus filtering out noisy backgrounds that may cause incorrect correspondences.
arXiv Detail & Related papers (2021-07-19T09:38:27Z) - An Efficient Diagnosis Algorithm for Inconsistent Constraint Sets [68.8204255655161]
We introduce a divide-and-conquer based diagnosis algorithm (FastDiag) which identifies minimal sets of faulty constraints in an over-constrained problem.
We compare FastDiag with the conflict-directed calculation of hitting sets and present an in-depth performance analysis.
arXiv Detail & Related papers (2021-02-17T19:55:42Z) - Constrained episodic reinforcement learning in concave-convex and
knapsack settings [81.08055425644037]
We provide a modular analysis with strong theoretical guarantees for settings with concave rewards and convex constraints.
Our experiments demonstrate that the proposed algorithm significantly outperforms these approaches in existing constrained episodic environments.
arXiv Detail & Related papers (2020-06-09T05:02:44Z) - Learn to Predict Sets Using Feed-Forward Neural Networks [63.91494644881925]
This paper addresses the task of set prediction using deep feed-forward neural networks.
We present a novel approach for learning to predict sets with unknown permutation and cardinality.
We demonstrate the validity of our set formulations on relevant vision problems.
arXiv Detail & Related papers (2020-01-30T01:52:07Z) - Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and
Co-Expansion [45.716171458483636]
corpus-based set expansion algorithms bootstrap the given seeds by incorporating lexical patterns and distributional similarity.
Set-CoExpan automatically generates auxiliary sets as negative sets that are closely related to the target set of user's interest.
We show that Set-CoExpan outperforms strong baseline methods significantly.
arXiv Detail & Related papers (2020-01-27T22:34:07Z)
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.