基于局部分区序积空间的广义多视图顺序三向决策

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jin Qian , Chuanpeng Zhou , Ying Yu , Mingchen Zheng , Chengxin Hong , Hui Wang
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引用次数: 0

摘要

层次序贯三向决策模型是解决复杂问题的一种方法。现有的分层顺序三向决策模型大多采用多视图和/或多级方法。然而,随着视图数量的增加和层次的加深,模型变得太大,无法有效地解决问题。为了解决这一问题,本文提出了一种基于局部划分顺序积空间模型的广义多视图分层顺序三向决策方法。具体来说,我们首先使用嵌套的分区序列来表示视图。接下来,根据层数拆分视图内各层之间的线性顺序关系,以获得局部线性顺序关系。然后,在多个视图中,利用笛卡尔积运算组合不同视图中彼此接近的层次之间的局部线性序关系,构造一个广义的局部划分序积空间。最后,通过对分层顺序三向决策进行积分,将广义局部划分顺序积空间转化为多视图分层顺序三向决策模型。在多数据集上的实验结果表明,与现有模型相比,本文提出的多视图分层顺序三向决策模型具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generalized multiview sequential three-way decisions based on local partition order product space
The hierarchical sequential three-way decision model is a method for addressing complex problem-solving. The existing hierarchical sequential three-way decision models mostly employ multi-view and/or multi-level approaches. However, as the number of views increases and the levels deepen, the model becomes too large to solve problems efficiently. In order to solve this problem, this paper proposes a generalized multiview hierarchical sequential three-way decisions based on local partition order product space model. Specifically, we first use a nested partition sequence to represent a view. Next, the linear order relations between levels within the views are split according to the number of levels to obtain local linear order relations. Then, in the multiple views, the local linear order relations between levels close to each other from different views are combined using Cartesian product operations to construct a generalized local partition order product space. Finally, by integrating the hierarchical sequential three-way decisions, the generalized local partition order product space is transformed into a multiview hierarchical sequential three-way decisions model. Experimental results on multiple datasets demonstrate that the proposed multiview hierarchical sequential three-way decision model achieves better performance compared to the existing models.
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来源期刊
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning 工程技术-计算机:人工智能
CiteScore
6.90
自引率
12.80%
发文量
170
审稿时长
67 days
期刊介绍: The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.
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