Evaluating score normalization methods in data fusion

Authors:
Shengli Wu;Fabio Crestani;Yaxin Bi
Affiliations:
School of Computing and Mathematics, University of Ulster, Northern Ireland, UK;Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK;School of Computing and Mathematics, University of Ulster, Northern Ireland, UK
Venue:
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
Year:
2006

Citing 8
Cited 8

Analyses of multiple evidence combination

Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking retrieval systems without relevance judgments

Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Models for metasearch

Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance score normalization for metasearch

Proceedings of the tenth international conference on Information and knowledge management
Data fusion with estimated weights

Proceedings of the eleventh international conference on Information and knowledge management
Methods for ranking information retrieval systems without relevance judgments

Proceedings of the 2003 ACM symposium on Applied computing
Scaling IR-system evaluation using term relevance sets

Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Performance prediction of data fusion for information retrieval

Information Processing and Management: an International Journal

Assigning appropriate weights for the linear combination data fusion method in information retrieval

Information Processing and Management: an International Journal
Concept-based feature generation and selection for information retrieval

AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Who should I cite: learning literature search models from citation behavior

CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Information fusion for combining visual and textual image retrieval in imageCLEF@ICPR

ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
The linear combination data fusion method in information retrieval

DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
Applying the data fusion technique to blog opinion retrieval

Expert Systems with Applications: An International Journal
Using the euclidean distance for retrieval evaluation

BNCOD'11 Proceedings of the 28th British national conference on Advances in databases
SmartVisionApp: A framework for computer vision applications on mobile devices

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

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.