The weighted Condorcet fusion in information retrieval

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. (C) 2012 Elsevier Ltd. All rights reserved.