Information Processing and Management: an International Journal
Information Processing and Management: an International Journal
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
Predicting the performance of linearly combined IR systems
Proceedings of the 21st 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
Condorcet fusion for improved retrieval
Proceedings of the eleventh international conference on Information and knowledge management
Fusion Via a Linear Combination of Scores
Information Retrieval
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
Assigning appropriate weights for the linear combination data fusion method in information retrieval
Information Processing and Management: an International Journal
An overview of text-independent speaker recognition: From features to supervectors
Speech Communication
Image fusion based on a new contourlet packet
Information Fusion
Estimating probabilities for effective data fusion
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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
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The Condorcet fusion is a distinctive fusion method and was found useful in information retrieval. Two basic requirements for the Condorcet fusion to improve retrieval effectiveness are: (1) all component systems involved should be more or less equally effective; and (2) each information retrieval system should be developed independently and thus each component result is more or less equally different from the others. These two requirements may not be satisfied in many cases, then weighted Condorcet becomes a good option. However, how to assign weights for the weighted Condorcet has not been investigated. In this paper, we present a linear discriminant analysis (LDA) based approach to training weights. Some properties of Condorcet fusion and weighted Condorcet fusion are discussed. Experiments are conducted with three groups of runs submitted to TREC to evaluate the performance of a group of data fusion methods. The empirical investigation finds that Condorcet fusion is a good ranking-based method in good conditions, while weighted Condorcet fusion can make significant improvement over Condorcet fusion when the conditions are not favourable for Condorcet fusion. The experiments also show that the proposed LDA weighting schema is effective and Condorcet fusion with LDA based weighting schema is more effective than all other data fusion methods involved.