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Evaluating Score Normalization Methods in Data Fusion

Wu, Shengli, Crestani, Fabio and Bi, Yaxin (2006) Evaluating Score Normalization Methods in Data Fusion. In: AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology. Springer-Verlag Berlin. 6 pp. [Conference contribution]

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Abstract

In data fusion, score normalization is a step to make scores, which are obtained from different component systems for all documents, comparable to each other. It is an indispensable step for effective data fusion algorithms such as CombSum and CombMNZ to combine them. In this paper, we evaluate four linear score normalization methods, namely the fitting method, Zero-one, Sum, and ZMUV, through extensive experiments. The experimental results show that the fitting method and Zero-one appear to be the two leading methods.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & 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
ID Code:25506
Deposited By: Dr Yaxin Bi
Deposited On:20 Jan 2016 15:35
Last Modified:20 Jan 2016 15:35

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