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A Robust Ranking Methodology Based on Diverse Calibration of AdaBoost

  • Róbert Busa-Fekete
  • Balázs Kégl
  • Tamás Éltető
  • György Szarvas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6911)

Abstract

In subset ranking, the goal is to learn a ranking function that approximates a gold standard partial ordering of a set of objects (in our case, relevance labels of a set of documents retrieved for the same query). In this paper we introduce a learning to rank approach to subset ranking based on multi-class classification. Our technique can be summarized in three major steps. First, a multi-class classification model (AdaBoost.MH) is trained to predict the relevance label of each object. Second, the trained model is calibrated using various calibration techniques to obtain diverse class probability estimates. Finally, the Bayes-scoring function (which optimizes the popular Information Retrieval performance measure NDCG), is approximated through mixing these estimates into an ultimate scoring function. An important novelty of our approach is that many different methods are applied to estimate the same probability distribution, and all these hypotheses are combined into an improved model. It is well known that mixing different conditional distributions according to a prior is usually more efficient than selecting one “optimal” distribution. Accordingly, using all the calibration techniques, our approach does not require the estimation of the best suited calibration method and is therefore less prone to overfitting. In an experimental study, our method outperformed many standard ranking algorithms on the LETOR benchmark datasets, most of which are based on significantly more complex learning to rank algorithms than ours.

Keywords

Learning-to-rank AdaBoost Class Probability Calibration 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Róbert Busa-Fekete
    • 1
    • 2
  • Balázs Kégl
    • 1
    • 3
  • Tamás Éltető
    • 3
  • György Szarvas
    • 2
    • 4
  1. 1.Linear Accelerator Laboratory (LAL)University of Paris-Sud, CNRSOrsayFrance
  2. 2.Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University of SzegedSzegedHungary
  3. 3.Computer Science Laboratory (LRI)University of Paris-Sud, CNRS and INRIA-SaclayOrsayFrance
  4. 4.Ubiquitous Knowledge Processing (UKP) Lab, Computer Science DepartmentTechnische Universität DarmstadtDarmstadtGermany

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