Name
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 $$\{0,1\}$$, 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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s10994-019-05826-x | Machine Learning |
Keywords | Field | DocType |
Ranking, Boosting, Ensemble methods, Rankboost | Ranking,Boosting (machine learning),Artificial intelligence,Ensemble learning,Mathematics,Machine learning,Binary number | Journal |
Volume | Issue | ISSN |
109 | 1 | 0885-6125 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Harold Connamacher | 1 | 2 | 0.76 |
| Nikil Pancha | 2 | 0 | 0.34 |
| Rui Liu | 3 | 0 | 1.01 |
| Soumya Ray | 4 | 94 | 8.89 |