Interval-valued q-rung orthopair fuzzy Weber operator and its group decision-making application
- URL: http://arxiv.org/abs/2410.19752v1
- Date: Fri, 11 Oct 2024 02:24:06 GMT
- Title: Interval-valued q-rung orthopair fuzzy Weber operator and its group decision-making application
- Authors: Benting Wana, Zhuocheng Wua, Mengjie Hanb, Minjun Wana,
- Abstract summary: We develop a Swing-based multi-attribute group decision-making (MAGDM) method under interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs)
We develop a MAGDM method for evaluating students' learning effectiveness using the IVq-ROFWOWA operator and the Swing algorithm.
- Score: 0.0
- License:
- Abstract: The evaluation of learning effectiveness requires the integration of objective test results and analysis of uncertain subjective evaluations. Fuzzy theory methods are suitable for handling fuzzy information and uncertainty to obtain comprehensive and accurate evaluation results. In this paper, we develop a Swing-based multi-attribute group decision-making (MAGDM) method under interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). Firstly, an extended interval-valued q rung orthopair Weber ordered weighted average (IVq-ROFWOWA) operator is introduced. Then the attribute weights deriving method is designed by using the optimized Swing algorithm. Furthermore, we develop a MAGDM method for evaluating students' learning effectiveness using the IVq-ROFWOWA operator and the Swing algorithm. Finally, a case of evaluating students' learning effectiveness is illustrated by using the proposed MAGDM method. The implementing results demonstrate that the proposed MAGDM method is feasible and effective, and the Swing algorithm enhances better differentiation in ranking alternatives compared to other methods.
Related papers
- Adaptive multiple optimal learning factors for neural network training [0.0]
The proposed Adaptive Multiple Optimal Learning Factors (AMOLF) algorithm dynamically adjusts the number of learning factors based on the error change per multiply.
The thesis also introduces techniques for grouping weights based on the curvature of the objective function and for compressing large Hessian matrices.
arXiv Detail & Related papers (2024-06-04T21:18:24Z) - Querying Easily Flip-flopped Samples for Deep Active Learning [63.62397322172216]
Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data.
One effective selection strategy is to base it on the model's predictive uncertainty, which can be interpreted as a measure of how informative a sample is.
This paper proposes the it least disagree metric (LDM) as the smallest probability of disagreement of the predicted label.
arXiv Detail & Related papers (2024-01-18T08:12:23Z) - MFABA: A More Faithful and Accelerated Boundary-based Attribution Method
for Deep Neural Networks [69.28125286491502]
We introduce MFABA, an attribution algorithm that adheres to axioms.
Results demonstrate its superiority by achieving over 101.5142 times faster speed than the state-of-the-art attribution algorithms.
arXiv Detail & Related papers (2023-12-21T07:48:15Z) - Bandit-Driven Batch Selection for Robust Learning under Label Noise [20.202806541218944]
We introduce a novel approach for batch selection in Gradient Descent (SGD) training, leveraging bandit algorithms.
Our methodology focuses on optimizing the learning process in the presence of label noise, a prevalent issue in real-world datasets.
arXiv Detail & Related papers (2023-10-31T19:19:01Z) - Improved Algorithms for Neural Active Learning [74.89097665112621]
We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting.
We introduce two regret metrics by minimizing the population loss that are more suitable in active learning than the one used in state-of-the-art (SOTA) related work.
arXiv Detail & Related papers (2022-10-02T05:03:38Z) - A integrating critic-waspas group decision making method under
interval-valued q-rung orthogonal fuzzy enviroment [0.0]
This paper provides a new tool for multi-attribute multi-objective group decision-making with unknown weights and attributes' weights.
An interval-valued generalized fuzzy group decision-making method is proposed based on the Yager operator and CRITIC-WASPAS method with unknown weights.
Its merits lie in allowing decision-makers greater freedom, avoiding bias due to decision-makers' weight, and yielding accurate evaluation.
arXiv Detail & Related papers (2022-01-04T08:11:28Z) - Unsupervised feature selection via self-paced learning and low-redundant
regularization [6.083524716031565]
An unsupervised feature selection is proposed by integrating the framework of self-paced learning and subspace learning.
The convergence of the method is proved theoretically and experimentally.
The experimental results show that the proposed method can improve the performance of clustering methods and outperform other compared algorithms.
arXiv Detail & Related papers (2021-12-14T08:28:19Z) - DEALIO: Data-Efficient Adversarial Learning for Imitation from
Observation [57.358212277226315]
In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator.
Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms.
This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk.
We propose a more data-efficient IfO algorithm
arXiv Detail & Related papers (2021-03-31T23:46:32Z) - Logistic Q-Learning [87.00813469969167]
We propose a new reinforcement learning algorithm derived from a regularized linear-programming formulation of optimal control in MDPs.
The main feature of our algorithm is a convex loss function for policy evaluation that serves as a theoretically sound alternative to the widely used squared Bellman error.
arXiv Detail & Related papers (2020-10-21T17:14:31Z) - Adaptive Gradient Method with Resilience and Momentum [120.83046824742455]
We propose an Adaptive Gradient Method with Resilience and Momentum (AdaRem)
AdaRem adjusts the parameter-wise learning rate according to whether the direction of one parameter changes in the past is aligned with the direction of the current gradient.
Our method outperforms previous adaptive learning rate-based algorithms in terms of the training speed and the test error.
arXiv Detail & Related papers (2020-10-21T14:49:00Z) - MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement
Learning [36.14516028564416]
This paper proposes an innovative Multiple Model Kalman Temporal Difference (MM-KTD) framework to learn optimal control policies.
An active learning method is proposed to enhance the sampling efficiency of the system.
Experimental results show superiority of the MM-KTD framework in comparison to its state-of-the-art counterparts.
arXiv Detail & Related papers (2020-05-30T06:39:55Z)
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.