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首页> 外文期刊>International journal of image and data fusion >An uncertainty measure and fusion rule for conflict evidences of big data via Dempster-Shafer theory
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An uncertainty measure and fusion rule for conflict evidences of big data via Dempster-Shafer theory

机译：基于Dempster-Shafer理论的大数据冲突证据的不确定性度量和融合规则

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摘要

We are living in a world surrounded by big data which can be often created by social networks, online and offline transactions, medical records and sensors. An appropriate treatment of big data can effect in enlightening, sharp and pertinent decision-making in numerous fields, like field of medical and healthcare, field of business, field of management and government. However, plenty of threats initiated by the characteristic of big data leads to the studying of big data. On the other hand, uncertainty measure of big data is a major task. Dempster-Shafer theory of evidence is an important tool of uncertainty modelling. In this paper, an effort has been made to propose an approach to measure uncertainty that involved in big data and a fusion rule of conflict evidences of big data. Finally, numerical examples are illustrated under these settings and results are compared with existing approaches.
机译：我们生活在一个大数据包围的世界中，大数据通常可以由社交网络，在线和离线交易，病历和传感器创建。对大数据的适当处理可以在许多领域（例如医疗和保健领域，业务领域，管理领域和政府领域）启发开明，敏锐而相关的决策。但是，大数据的特征引发的大量威胁导致对大数据的研究。另一方面，大数据的不确定性度量是一项主要任务。证据的Dempster-Shafer理论是不确定性建模的重要工具。在本文中，已努力提出一种方法来测量涉及大数据的不确定性和大数据冲突证据的融合规则。最后，在这些设置下说明了数值示例，并将结果与​​现有方法进行了比较。

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