TRIBE:基于证据的信息信任修正

M. Sensoy, Geeth de Mel, Lance M. Kaplan, T. Pham, T. Norman
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引用次数: 12

摘要

近年来,可用于支持决策的信息来源的数量急剧增加。然而,信息源越多并不意味着融合信息的精度越高。这部分是由于这些来源中的一些可能是错误的或恶意的。因此,在进行融合之前,对信息的信任进行评估是至关重要的。为了估计信息的可信度,现有的方法使用信息来源的可信度作为代理。我们认为,信息之间的冲突也可能作为证据,以减少对信息的信任。在本文中,我们使用主观意见来表示来自不同来源的信息。我们建议利用意见之间的冲突来修正其可信度。为此,我们将信任修正形式化为约束优化问题。通过广泛的实证研究,我们表明我们的方法在面对恶意信息源时明显优于现有的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TRIBE: Trust revision for information based on evidence
In recent years, the number of information sources available to support decision-making has increased dramatically. However, more information sources do not always mean higher precision in the fused information. This is partially due to the fact that some of these sources may be erroneous or malicious. Therefore, it is critical to asses the trust in information before performing fusion. To estimate trust in information, existing approaches use trustworthiness of its source as a proxy. We argue that conflicts between information may also serve as evidence to reduce trust in information. In this paper, we use subjective opinions to represent information from diverse sources. We propose to exploit conflicts between opinions to revise their trustworthiness. For this purpose, we formalise trust revision as a constraint optimisation problem. Through extensive empirical studies, we show that our approach significantly outperform existing ones in the face of malicious information sources.
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