The weighted Condorcet fusion in information retrieval

The Condorcet fusion is a distinctive fusion method and was found usefulin information retrieval. Two basic requirements for the Condorcet fusion to improveretrieval effectiveness are: 1. all component systems involved should be more or less equally effective; 2. each information retrieval system should be developedindependently and thus each component result is more or less equally differentfrom the others. These two requirements may not be satisfied in many cases, thenweighted 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 approachto training weights. Some properties of Condorcet fusionand weighted Condorcet fusion are discussed. Experiments are conductedwith 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 alsoshow 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.