Evaluating Score Normalization Methods in Data Fusion

Shengli Wu, Fabio Crestani, Yaxin Bi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

38 Citations (Scopus)

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages6
Publication statusPublished - 2006
EventAIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology -
Duration: 1 Jan 2006 → …

Conference

ConferenceAIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
Period1/01/06 → …

Fingerprint

Data fusion
Experiments

Cite this

Wu, S., Crestani, F., & Bi, Y. (2006). Evaluating Score Normalization Methods in Data Fusion. In Unknown Host Publication
Wu, Shengli ; Crestani, Fabio ; Bi, Yaxin. / Evaluating Score Normalization Methods in Data Fusion. Unknown Host Publication. 2006.
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title = "Evaluating Score Normalization Methods in Data Fusion",
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.",
author = "Shengli Wu and Fabio Crestani and Yaxin Bi",
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Wu, S, Crestani, F & Bi, Y 2006, Evaluating Score Normalization Methods in Data Fusion. in Unknown Host Publication. AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology, 1/01/06.

Evaluating Score Normalization Methods in Data Fusion. / Wu, Shengli; Crestani, Fabio; Bi, Yaxin.

Unknown Host Publication. 2006.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Evaluating Score Normalization Methods in Data Fusion

AU - Wu, Shengli

AU - Crestani, Fabio

AU - Bi, Yaxin

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

M3 - Conference contribution

BT - Unknown Host Publication

ER -

Wu S, Crestani F, Bi Y. Evaluating Score Normalization Methods in Data Fusion. In Unknown Host Publication. 2006