  Data fusion is the combination of the results of independent searches on a
document collection into one single output result set. It has been shown in the
past that this can greatly improve retrieval effectiveness over that of the
individual results.
  This paper presents probFuse, a probabilistic approach to data fusion.
ProbFuse assumes that the performance of the individual input systems on a
number of training queries is indicative of their future performance. The fused
result set is based on probabilities of relevance calculated during this
training process. Retrieval experiments using data from the TREC ad hoc
collection demonstrate that probFuse achieves results superior to that of the
popular CombMNZ fusion algorithm.
