Rankboost+: An Improvement to Rankboost

Abstract

Rankboost is a well-known algorithm that iteratively creates and aggregates a collection of “weak rankers” to build an effective ranking procedure. Initial work on Rankboost proposed two variants. One variant, that we call Rb-d and which is designed for the scenario where all weak rankers have the binary range $\{0,1\}$ 0 , 1 , has good theoretical properties, but does not perform well in practice. The other, that we call Rb-c , has good empirical behavior and is the recommended variation for this binary weak ranker scenario but lacks a theoretical grounding. In this paper, we rectify this situation by proposing an improved Rankboost algorithm for the binary weak ranker scenario that we call Rankboost $+$ + . We prove that this approach is theoretically sound and also show empirically that it outperforms both Rankboost variants in practice. Further, the theory behind Rankboost $+$ + helps us to explain why Rb-d may not perform well in practice, and why Rb-c is better behaved in the binary weak ranker scenario, as has been observed in prior work.