A maximum agreement approach to information fusion
Authors: 
M. Y. Cai, Y. Lin, C. L. Liu, C. L. Ji, W. J. ZhangAuthors Info & Claims
M. Y. Cai, Y. Lin, C. L. Liu, C. L. Ji, W. J. ZhangAuthors Info & ClaimsArticle No.: 125, Pages 1 - 4
Abstract
Information fusion is a generic technique for the problem of information fusion. The common features with this problem are (1) there is a target system X and its state (S) is to be inferred or predicted, (2) there is a group of sensors which have a varying degree of imprecise connection with S of X, and (3) there is a need to come up with an agreed inference or prediction on the state of X (note that consensus here does not mean all with the same opinion by the group). Elsewhere, we proposed an approach called "maximum agreement (MA)" to information fusion in general and probability distribution function aggregation in specific. The basic idea of MA is that an agreed inference is a function of individual sensors' measurements and the agreed measurement can be determined based on the goal that the agreed judgment has a maximum consensus with all individual sensors' measurements. In this paper, we show some alternative methods of MA and discuss their characteristics with reference to MA. We shall then conclude that MA is the best method among all the alternative methods for the problem of expert opinion aggregation or consensus aggregation.
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Information & Contributors
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Published In
March 2017
1349 pages
ISBN:9781450347747
DOI:10.1145/3018896
- General Chair:
- Hani Hamdan,
- Program Chairs:
- Homero Toral-Cruz,
- Sedat Akleylek,
- Hamid Mcheick,
- Publications Chair:
- Djallel Eddine Boubiche
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Published: 22 March 2017
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ICC '17
ICC '17: Second International Conference on Internet of Things, Data and Cloud Computing
March 22 - 23, 2017
Cambridge, United Kingdom
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ICC '17 Paper Acceptance Rate 213 of 590 submissions, 36%;
Overall Acceptance Rate 213 of 590 submissions, 36%
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