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An empirical comparison of learning algorithms for nonparametric scoring: the TreeRank algorithm and other methods

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Abstract

The TreeRank algorithm was recently proposed in [1] and [2] as a scoring-based method based on recursive partitioning of the input space. This tree induction algorithm builds orderings by recursively optimizing the Receiver Operating Characteristic curve through a one-step optimization procedure called LeafRank. One of the aim of this paper is the in-depth analysis of the empirical performance of the variants of TreeRank/LeafRank method. Numerical experiments based on both artificial and real data sets are provided. Further experiments using resampling and randomization, in the spirit of bagging and random forests are developed [3, 4] and we show how they increase both stability and accuracy in bipartite ranking. Moreover, an empirical comparison with other efficient scoring algorithms such as RankBoost and RankSVM is presented on UCI benchmark data sets.

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Acknowledgments

We warmly thank Cynthia Rudin who kindly provided the code for the P-norm Push algorithm.

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Authors and Affiliations

  1. Télécom ParisTech, LTCI, UMR CNRS, 5141, Paris, France

    Stéphan Clémençon & Marine Depecker

  2. ENS Cachan, UniverSud, CMLA, UMR CNRS, 8536, Cachan, France

    Nicolas Vayatis

Authors
  1. Stéphan Clémençon
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  2. Marine Depecker
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  3. Nicolas Vayatis
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Correspondence to Marine Depecker.

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Clémençon, S., Depecker, M. & Vayatis, N. An empirical comparison of learning algorithms for nonparametric scoring: the TreeRank algorithm and other methods. Pattern Anal Applic 16, 475–496 (2013). https://doi.org/10.1007/s10044-012-0299-1

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  • DOI: https://doi.org/10.1007/s10044-012-0299-1

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