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 $$\{0,1\}$$, 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.

中文翻译：

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Rankboost $$+$$ + : Rankboost 的改进

Rankboost 是一种众所周知的算法，它迭代地创建和聚合一组“弱排名器”以构建有效的排名程序。Rankboost 的初步工作提出了两种变体。一种变体，我们称之为 Rb-d，专为所有弱排名器都具有二进制范围 $$\{0,1\}$$ 的场景而设计，具有良好的理论特性，但在实践中表现不佳。另一个，我们称之为 Rb-c，具有良好的经验行为，是这种二元弱排名方案的推荐变体，但缺乏理论基础。在本文中，我们通过为我们称为 Rankboost$$+$$ 的二元弱排名器场景提出一种改进的 Rankboost 算法来纠正这种情况。我们证明这种方法在理论上是合理的，并且从经验上也表明它在实践中优于两种 Rankboost 变体。更多，