Learning to Rank with Nonlinear Monotonic Ensemble

  • Nikita Spirin
  • Konstantin Vorontsov
Conference paper

DOI: 10.1007/978-3-642-21557-5_4

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6713)
Cite this paper as:
Spirin N., Vorontsov K. (2011) Learning to Rank with Nonlinear Monotonic Ensemble. In: Sansone C., Kittler J., Roli F. (eds) Multiple Classifier Systems. MCS 2011. Lecture Notes in Computer Science, vol 6713. Springer, Berlin, Heidelberg


Over the last decade learning to rank (L2R) has gained a lot of attention and many algorithms have been proposed. One of the most successful approach is to build an algorithm following the ensemble principle. Boosting is the key representative of this approach. However, even boosting isn’t effective when used to increase the performance of individually strong algorithms, scenario when we want to blend already successful L2R algorithms in order to gain an additional benefit. To address this problem we propose a novel algorithm, based on a theory of nonlinear monotonic ensembles, which is able to blend strong base rankers effectively. Specifically, we provide the concept of defect of a set of algorithms that allows to deduce a popular pairwise approach in strict mathematical terms. Using the concept of defect, we formulate an optimization problem and propose a sound method of its solution. Finally, we conduct experiments with real data which shows the effectiveness of the proposed approach.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nikita Spirin
    • 1
  • Konstantin Vorontsov
    • 2
  1. 1.University of IllinoisUrbana-ChampaignUSA
  2. 2.Dorodnicyn Computing Center of the Russian Academy of SciencesRussia

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