Conference proceeding
Combination of Granules, Rough Sets With Evidence Theory and Its Application in Incomplete Data Fusion for Belief Estimation
2008 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, VOLS 1 AND 2, pp 653-658
01 Jan 2008
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Abstract
This paper presents an approach to deal with multi sensor data fusion problem in incomplete circumstance using combination of granule idea, rough approximation and evidence theory. It deletes redundant sensors through rough set theory in selecting and reducing features, and forming dominant characters to form various granules. It applies these granules to establish belief functions to get different belief estimates. It extracts decision rules from incomplete system to identify targets. Experiments show this method can overcome slow problem in posing massive data set with fluctuant sensors and prove to be feasible and efficient.
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Details
- Title
- Combination of Granules, Rough Sets With Evidence Theory and Its Application in Incomplete Data Fusion for Belief Estimation
- Creators
- Chen Wu - Jiangsu UniversityXiaohua Hu - Drexel University, Information ScienceEnbin Wang - Jiangsu Univ Sci & Technol, Sch Elect & Informat, Zhenjiang 212003, Jiangsu Prov, Peoples R China
- Publication Details
- 2008 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, VOLS 1 AND 2, pp 653-658
- Publisher
- IEEE
- Number of pages
- 2
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000263829500144
- Scopus ID
- 2-s2.0-57949116346
- Other Identifier
- 991019167542804721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods