NDLI: An efficient algorithm for identification of real belief measures

Content Provider	IEEE Xplore Digital Library
Author	Wei Chen Kajia Cao Renan Jia Kuiliang Chen
Copyright Year	2009
Description	Author affiliation: Dept of Computer Science, University of Nebraska at Omaha, USA (Wei Chen) \|\| Penn Center for Bioinformatics, University of Pennsylvania, Philadelphia, USA (Kajia Cao) \|\| Institute of Systems Engineering, Nanchang University, Jiangxi, China (Renan Jia; Kuiliang Chen)
Abstract	Belief measures are widely applied to management of uncertainty in information fusion. In most published applications, the estimations of belief measures that come from empirical rescouses, such as expert systems, are considered to be real belief measures without any validation. We proposed an efficient algorithm that can quickly detect the contradiction between the estimation and requirements of a real belief measure and adjust the estimation accordingly. The contradiction is assessed by a probability assignment and the estimation is adjusted by Genetic Algorithm. We tested the algorithm using two different simulations. As a result, it shows that the proposed algorithm successfully identified the real belief measures.
Starting Page	83
Ending Page	87
File Size	170467
Page Count	5
File Format	PDF
ISBN	9781424448302
DOI	10.1109/GRC.2009.5255156
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Current measurement Engineering management Systems engineering and theory Inference algorithms Bioinformatics Expert systems Modeling Genetic algorithms Testing
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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