利用多粒度信念结构进行证据组合以实现模式分类

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Sciences Pub Date : 2024-10-22 DOI:10.1016/j.ins.2024.121577
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引用次数: 0

摘要

信念函数(BF)理论为有效建模、量化不确定性和组合证据提供了一个框架,使其成为解决不确定决策问题的有力工具。然而,随着判别框架的扩大,融合过程中处理的焦点要素数量不断增加,导致计算复杂度迅速上升,从而限制了信念函数理论的实际应用。为了克服这一问题,本研究提出了一种新颖的多粒度信念结构(MGBS)方法。MGBS 的构建减少了焦点元素的数量,保留了基本信念分配中的关键信息。这有效降低了融合的计算复杂度,同时确保了尽可能高的分类精度。我们将提出的 MGBS 算法应用于人类活动识别任务,并使用加州大学欧文分校的 mHealth、PAMAP2 和智能手机数据集验证了该算法的有效性。
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Evidence combination with multi-granularity belief structure for pattern classification
Belief function (BF) theory provides a framework for effective modeling, quantifying uncertainty, and combining evidence, rendering it a potent tool for tackling uncertain decision-making problems. However, with the expansion of the frame of discernment, the increasing number of focal elements processed during the fusion procedure leads to a rapid increase in computational complexity, which limits the practical application of BF theory. To overcome this issue, a novel multi-granularity belief structure (MGBS) method was proposed in this study. The construction of MGBS reduced the number of focal elements and preserved crucial information in the basic belief assignment. This effectively reduced the computational complexity of fusion while ensuring the highest possible classification accuracy. We applied the proposed MGBS algorithm to a human activity recognition task and verified its effectiveness using the University of California, Irvine mHealth, PAMAP2, and Smartphone datasets.
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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