An uncertainty measure and fusion rule for conflict evidences of big data via Dempster–Shafer theory. (3rd April 2018)

• Record Type: Journal Article
• Authors: Dutta, Palash
• Is Part Of: International journal of image and data fusion. Volume 9:Number 2(2018)
• Page Start: 152
• Page End: 169
• Publication Date: 2018-04-03
• Subjects: Big data -- Dempster–Shafer theory -- Dempster–Shafer structure (DSS) -- measure of uncertainty -- Dempster's rule of combination; Image processing -- Periodicals; Multisensor data fusion -- Periodicals; Multisensor data fusion; Periodicals; 621.36705
• DOI: 10.1080/19479832.2017.1391336
• Languages: English
• ISSNs: 1947-9832

Abstract

ABSTRACT: 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.