Foundational propositions of hesitant fuzzy soft $\beta$-covering approximation spaces

• URL: http://arxiv.org/abs/2403.05290v1
• Date: Fri, 8 Mar 2024 13:16:17 GMT
• Title: Foundational propositions of hesitant fuzzy soft $\beta$-covering approximation spaces
• Authors: Shizhan Lu
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Soft set theory serves as a mathematical framework for handling uncertain information, and hesitant fuzzy sets find extensive application in scenarios involving uncertainty and hesitation. 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, which are formulated based on distinct forms of inclusion relationships among hesitancy fuzzy sets. Subsequently, several associated properties are investigated. Additionally, specific variations of hesitant fuzzy soft $\beta$-coverings are introduced by incorporating hesitant fuzzy rough sets, followed by an exploration of properties pertaining to hesitant fuzzy soft $\beta$-covering approximation spaces.

Related papers

• Learning by Neighbor-Aware Semantics, Deciding by Open-form Flows: Towards Robust Zero-Shot Skeleton Action Recognition [41.77490816513839]
  We propose a novel method for zero-shot skeleton action recognition, termed $texttt$textbfFlora$$.<n>Specifically, we attune textual semantics by incorporating direction-aware regional semantics, and a cross-modal consistency objective.<n>Experiments on three benchmark datasets validate the effectiveness of our method, showing particularly impressive performance even when trained with only 10% of the seen data.
  arXiv  Detail & Related papers  (2025-11-12T14:54:53Z)
• Decision-making algorithm based on the energy of interval-valued fuzzy soft sets [0.0]
  We introduce the concept of energy of an interval-valued fuzzy soft set, as well as pessimistic and optimistic energy, enabling us to construct an effective decision-making algorithm.
  arXiv  Detail & Related papers  (2024-05-17T09:54:44Z)
• Interval-valued fuzzy soft $β$-covering approximation spaces [0.0]
  The concept of interval-valued fuzzy soft $beta$-covering approximation spaces (IFS$beta$CASs) is introduced. The relationships of four kinds of interval-valued fuzzy soft $beta$-coverings based fuzzy rough sets are investigated.
  arXiv  Detail & Related papers  (2024-04-03T13:04:54Z)
• Fuzzy Rough Choquet Distances for Classification [0.6445605125467574]
  This paper introduces a novel Choquet distance using fuzzy rough set based measures. The proposed measure combines the attribute information received from fuzzy rough set theory with the flexibility of the Choquet integral.
  arXiv  Detail & Related papers  (2024-03-18T14:53:48Z)
• Foundational theories of hesitant fuzzy sets and hesitant fuzzy information systems and their applications for multi-strength intelligent classifiers [32.78664473821173]
  Hesitant fuzzy sets are widely used in certain instances of uncertainty and hesitation. As a kind of set, hesitant fuzzy sets require an explicit definition of inclusion relationship.
  arXiv  Detail & Related papers  (2023-11-07T14:03:28Z)
• Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits [53.281230333364505]
  This paper studies the problem of contextual dueling bandits, where the binary comparison of dueling arms is generated from a generalized linear model (GLM) We propose a new SupLinUCB-type algorithm that enjoys computational efficiency and a variance-aware regret bound $tilde Obig(dsqrtsum_t=1Tsigma_t2 + dbig)$. Our regret bound naturally aligns with the intuitive expectation in scenarios where the comparison is deterministic, the algorithm only suffers from an $tilde O(d)$ regret.
  arXiv  Detail & Related papers  (2023-10-02T08:15:52Z)
• Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization [73.04187954213471]
  We introduce a unified learning approach to simultaneously modeling the coarse- and fine-grained retrieval. The proposed method has achieved +4.03%, +3.38%, and +2.40% Recall@50 accuracy over a strong baseline.
  arXiv  Detail & Related papers  (2022-11-14T14:25:40Z)
• Some neighborhood-related fuzzy covering-based rough set models and their applications for decision making [8.270779659551431]
  We propose four types of fuzzy neighborhood operators based on fuzzy covering by overlap functions and their implicators. A novel fuzzy TOPSIS methodology is put forward to solve a biosynthetic nanomaterials select issue.
  arXiv  Detail & Related papers  (2022-05-13T05:02:53Z)
• High-Dimensional Sparse Linear Bandits [67.9378546011416]
  We derive a novel $Omega(n2/3)$ dimension-free minimax regret lower bound for sparse linear bandits in the data-poor regime. We also prove a dimension-free $O(sqrtn)$ regret upper bound under an additional assumption on the magnitude of the signal for relevant features.
  arXiv  Detail & Related papers  (2020-11-08T16:48:11Z)
• Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards [24.983866845065926]
  We consider multi-armed bandits with heavy-tailed rewards, whose $p$-th moment is bounded by a constant $nu_p$ for $1pleq2$. We propose a novel robust estimator which does not require $nu_p$ as prior information. We show that an error probability of the proposed estimator decays exponentially fast.
  arXiv  Detail & Related papers  (2020-10-24T10:44:02Z)
• Relative Pose Estimation of Calibrated Cameras with Known $\mathrm{SE}(3)$ Invariants [65.2314683780204]
  We present a complete study of the relative pose estimation problem for a camera constrained by known $mathrmSE(3)$ invariants. These problems reduces the minimal number of point pairs for relative pose estimation. Experiments on synthetic and real data shows performance improvement compared to conventional relative pose estimation methods.
  arXiv  Detail & Related papers  (2020-07-15T13:55:55Z)
• Stochastic Linear Bandits Robust to Adversarial Attacks [117.665995707568]
  We provide two variants of a Robust Phased Elimination algorithm, one that knows $C$ and one that does not. We show that both variants attain near-optimal regret in the non-corrupted case $C = 0$, while incurring additional additive terms respectively. In a contextual setting, we show that a simple greedy algorithm is provably robust with a near-optimal additive regret term, despite performing no explicit exploration and not knowing $C$.
  arXiv  Detail & Related papers  (2020-07-07T09:00:57Z)
• Gradient Estimation with Stochastic Softmax Tricks [84.68686389163153]
  We introduce softmax tricks, which generalize the Gumbel-Softmax trick to spaces. We find that softmax tricks can be used to train latent variable models that perform better and discover more latent structure.
  arXiv  Detail & Related papers  (2020-06-15T00:43:44Z)
• Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis [48.25118610209545]
  We consider a smoothed setting for structured linear contextual bandits where the adversarial contexts are perturbed by Gaussian noise. We show there is implicit exploration in the smoothed setting where a simple greedy algorithm works.
  arXiv  Detail & Related papers  (2020-02-26T07:29:24Z)

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.