Rank aggregation using active learning in meta-searching

Existing methods dealing with the problem of rank aggregation in the context of meta-search in information retrieval are considered as a passive learner machine and suffer in the presence of unreliable ranking lists. This paper proposes a novel approach which selects the most informative features and instances to be labeled from which the model will learn. To train an efficient ranking aggregation model from few labeled instances, we use Multiple Hyperplane Ranker algorithm in an active learning environment. Experimental results on the OHSUMED dataset show that our method outperforms the existing methods.