Automatic combination of multiple ranked retrieval systems
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Predicting the performance of linearly combined IR systems
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Ranking retrieval systems without relevance judgments
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Modeling score distributions for combining the outputs of search engines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
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
Modern Information Retrieval
Condorcet fusion for improved retrieval
Proceedings of the eleventh international conference on Information and knowledge management
Data fusion with estimated weights
Proceedings of the eleventh international conference on Information and knowledge management
Fusion Via a Linear Combination of Scores
Information Retrieval
From Retrieval Status Values to Probabilities of Relevance for Advanced IR Applications
Information Retrieval
Methods for ranking information retrieval systems without relevance judgments
Proceedings of the 2003 ACM symposium on Applied computing
Web metasearch: rank vs. score based rank aggregation methods
Proceedings of the 2003 ACM symposium on Applied computing
Automatic ranking of information retrieval systems using data fusion
Information Processing and Management: an International Journal
Performance prediction of data fusion for information retrieval
Information Processing and Management: an International Journal
ProbFuse: a probabilistic approach to data fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Improving high accuracy retrieval by eliminating the uneven correlation effect in data fusion
Journal of the American Society for Information Science and Technology
Result merging methods in distributed information retrieval with overlapping databases
Information Retrieval
An outranking approach for rank aggregation in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Applying statistical principles to data fusion in information retrieval
Expert Systems with Applications: An International Journal
Evaluating score normalization methods in data fusion
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
Data fusion with correlation weights
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
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
Linear combination of component results in information retrieval
Data & Knowledge Engineering
The weighted Condorcet fusion in information retrieval
Information Processing and Management: an International Journal
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In data fusion, the linear combination method is a very flexible method since different weights can be assigned to different systems. However, it remains an open question which weighting schema should be used. In some previous investigations and experiments, a simple weighting schema was used: for a system, its weight is assigned as its average performance over a group of training queries. However, it is not clear if this weighting schema is good or not. In some other investigations, different numerical optimisation methods were used to search for appropriate weights for the component systems. One major problem with those numerical optimisation methods is their low efficiency. It might not be feasible to use them in some situations, for example in some dynamic environments, system weights need to be updated from time to time for reasonably good performance. In this paper, we investigate the weighting issue by extensive experiments. The key point is to try to find the relation between performances of component systems and their corresponding weights which can lead to good fusion performance. We demonstrate that a series of power functions of average performance, which can be implemented as efficiently as the simple weighting schema, is more effective than the simple weighting schema for the linear data fusion method. Some other features of the power function weighting schema and the linear combination method are also investigated. The observations obtained from this study can be used directly in fusion applications of component retrieval results. The observations are also very useful for optimisation methods to choose better starting points and therefore to obtain more effective weights more quickly.