In this paper, we develop a classification algorithm for finding the optimal rank aggregation algorithm. The input features for the classification are measures of noise and misinformation in the rankers. The optimal ranking algorithm varies greatly with respect to these two factors. We develop two measures to compute noise and misinformation: cluster quality and rank variance. Further, we develop a cost based decision method to find the least risky aggregator for a new set of ranked lists and show that this decision method outperforms any static rank aggregation method by through rigorous experimentation.

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