设计可能性信息融合——联想性、一致性和冗余的重要性

Christoph-Alexander Holst , V. Lohweg
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引用次数: 2

摘要

设计信息融合系统的主要挑战之一是确定信息聚合的结构和顺序。构建拓扑的关键标准包括融合规则的关联性以及信息源的一致性和冗余性。基于这些标准的融合拓扑设计灵活,产生最大的特定信息,并且对不可靠或有缺陷的源具有鲁棒性。在本文中,详细介绍了一种用于可能性信息融合拓扑的自动数据驱动设计方法,该方法显式地考虑了关联性、一致性和冗余性。提出的设计旨在处理认知不确定性,即即使在缺乏训练数据的情况下也能产生健壮的拓扑。融合设计方法在从技术系统获得的选定的公开可用的真实世界数据集上进行评估。通过保留部分训练数据来模拟认知不确定性。结果表明,在这种情况下,一致性作为唯一的设计标准会导致拓扑不健壮。在认知不确定性的情况下,包含冗余度量可以提高鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Designing Possibilistic Information Fusion—The Importance of Associativity, Consistency, and Redundancy
One of the main challenges in designing information fusion systems is to decide on the structure and order in which information is aggregated. The key criteria by which topologies are constructed include the associativity of fusion rules as well as the consistency and redundancy of information sources. Fusion topologies regarding these criteria are flexible in design, produce maximal specific information, and are robust against unreliable or defective sources. In this article, an automated data-driven design approach for possibilistic information fusion topologies is detailed that explicitly considers associativity, consistency, and redundancy. The proposed design is intended to handle epistemic uncertainty—that is, to result in robust topologies even in the case of lacking training data. The fusion design approach is evaluated on selected publicly available real-world datasets obtained from technical systems. Epistemic uncertainty is simulated by withholding parts of the training data. It is shown that, in this context, consistency as the sole design criterion results in topologies that are not robust. Including a redundancy metric leads to an improved robustness in the case of epistemic uncertainty.
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