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Applying statistical principles to data fusion in information retrieval

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Wu, Shengli, Bi, Yaxin and McClean, Sally (2007) Applying statistical principles to data fusion in information retrieval. In: IEEE International Conference on Systems, Man and Cybernetics, 2007 , Montreal, Canada. IEEE. 7 pp. [Conference contribution]

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URL: http://dx.doi.org/10.1109/ICSMC.2007.4413590

DOI: doi:10.1109/ICSMC.2007.4413590

Abstract

Data fusion in information retrieval has been investigated by many researchers and quite a few data fusion methods have been proposed. However, their impact on effectiveness has not been well understood. In this paper, we apply statistical principles to data fusion and present a statistical data fusion model, which specifies the algorithm for fusion and conditions to be satisfied. The statistical model can be used as a guideline for data fusion methods. Based on this analysis, we compare CombSum and CombMNZ, which are the two best-known data fusion methods. We explain why sometimes CombMNZ does outperform Comb- Sum and what can be done to make CombSum more effective. Experimental results with TREC data are reported to support the conclusion that our enhancements to the algorithm improve effectiveness.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
Computer Science Research Institute > Information and Communication Engineering
ID Code:14636
Deposited By:Professor Sally McClean
Deposited On:11 Aug 2010 15:43
Last Modified:11 Aug 2010 15:43

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