*** sorry if cross-posting ***
The 20^th International Conference on Information Fusion (FUSION 2017)
will be held in Xi’an, China at the Wyndham Hotel on *July 10–13, 2017*.
For updated information, please visit the official website:
http://www.fusion2017.org <http://www.fusion2017.org/>
We propose a special session on Big data information fusion with the
theory of belief functions.
*Description:* Big data has gained become a hotspot in both theoretical
research and real applications in the latest 10 years due to the rapid
expansion of the massive amount of data that is available for solving
different tasks within many different application domains. Big data is
so large or complex that traditional data processing applications are
inadequate to deal with them. One significant aspect is the uncertainty
incorporated in the Big data.
The theory of belief function is a powerful tool for uncertainty
modeling and reasoning, which is expected to provide effective solutions
for the Big data processing and analyses in information fusion related
applications. Therefore, we propose to organize such a special session
including the subtopics as follows:
- Belief function based analysis of complex networks
- Belief function based decision making
- Belief function based clustering analysis
- Belief function based pattern classification
- Belief function based feature selection/extraction
- Other topics in belief function based applications in information fusion
*Important Dates:***
- Full paper submission deadline: March 22, 2017
- Notification of acceptance: 1 May, 2017
*
**Submission:*
Authors submitting to a special session need to delay their submission
until the special sessions are available.
SS1: Big Data Information Fusion with the Theory of Belief Functions
<http://www.fusion2017.org/Special_Sessions.html#tm-section-1>
*Organizers: *Arnaud Martin (France), Deqiang Han (China)
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Pr. Arnaud MARTIN
Université de Rennes 1 / IUT de Lannion
UMR 6074 IRISA
Rue Edouard Branly BP 30219
22302 Lannion cedex, France
Phone: +(33)2.96.46.94.60
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