92
Views
5
CrossRef citations to date
0
Altmetric
Original Articles

The TreeRank Tournament algorithm for multipartite ranking

&
Pages 107-126 | Received 27 Nov 2013, Accepted 12 Sep 2014, Published online: 10 Oct 2014

References

  • S. Agarwal (2008), ‘Generalization Bounds for Some Ordinal Regression Algorithms’, in Proceedings of the 19th International Conference on Algorithmic Learning Theory, Budapest, Hungary, pp. 7–21.
  • S. Agarwal, T. Graepel, R. Herbrich, S. Har-Peled, and D. Roth ( 2005), ‘Generalization Bounds for the Area Under the ROC Curve’, Journal of Machine Learning Research, 6, 393–425.
  • L. Breiman (2001), ‘Random Forests’, Machine Learning, 45, 5–32. doi: 10.1023/A:1010933404324
  • S. Clémençon, M. Depecker, and N. Vayatis ( 2011), ‘Adaptive Partitioning Schemes for Bipartite Ranking’, Machine Learning, 43, 31–69. doi: 10.1007/s10994-010-5190-y
  • S. Clémençon, M. Depecker, and N. Vayatis ( 2012), ‘An Empirical Comparison of Learning Algorithms for Nonparametric Scoring: The TreeRank Algorithm and Other Methods’, Pattern Analysis and Applications, 16, 475–496. doi: 10.1007/s10044-012-0299-1
  • S. Clémençon, M. Depecker, and N. Vayatis ( 2013), ‘Ranking Forests’, Journal of Machine Learning Research, 14, 39–73.
  • S. Clémençon, G. Lugosi, and N. Vayatis ( 2008), ‘Ranking and Empirical Risk Minimization of U-Statistics’, Annals of Statistics, 36, 844–874. doi: 10.1214/009052607000000910
  • S. Clémençon, S. Robbiano, and N. Vayatis ( 2013), ‘Ranking Data with Ordinal Labels: Optimality and Pairwise Aggregation’, Machine Learning, 91, 67–104. doi: 10.1007/s10994-012-5325-4
  • S. Clémençon, and N. Vayatis ( 2009), ‘Tree-Based Ranking Methods’, IEEE Transactions on Information Theory, 55, 4316–4336. doi: 10.1109/TIT.2009.2025558
  • S. Clémençon, and N. Vayatis ( 2010), ‘Overlaying Classifiers: A Practical Approach for Optimal Scoring’, Constructive Approximation, 32, 619–648. doi: 10.1007/s00365-010-9084-9
  • A.B. David (2008), ‘Ordinal Real-World Data Sets Repository’. http://www.cs.waikato.ac.nz/ml/weka/datasets.html (accessed 24 June 2010).
  • A. Frank, and A. Asuncion ( 2010), ‘UCI Machine Learning Repository’. http://archive.ics.uci.edu/ml (accessed 2 October 2011).
  • Y. Freund, R.D. Iyer, R.E. Schapire, and Y. Singer ( 2003), ‘An Efficient Boosting Algorithm for Combining Preferences’, Journal of Machine Learning Research, 4, 933–969.
  • J.H. Friedman, T. Hastie, and R. Tibshirani ( 2010), ‘Regularization Paths for Generalized Linear Models via Coordinate Descent’, Journal of Statistical Software, 33, 1–22.
  • R. Herbrich, T. Graepel, and K. Obermayer ( 2000), ‘Large Margin Rank Boundaries for Ordinal Regression’, in Advances in Large Margin Classifiers, eds. A.J. Smola, P.L. Bartlett, B. Schölkopf, D. Schuurmans, Cambridge, MA: MIT Press, pp. 115–132.
  • E. Hüllermeier, J. Fürnkranz, W. Cheng, and K. Brinker ( 2008), ‘Label Ranking by Learning Pairwise Preferences’, Artificial Intelligence, 172, 1897–1917. doi: 10.1016/j.artint.2008.08.002
  • T. Pahikkala, E. Tsivtsivadze, A. Airola, J. Boberg, and T. Salakoski ( 2007), ‘Learning to Rank with Pairwise Regularized Least-Squares’, in Proceedings of SIGIR, Amsterdam, Netherlands, pp. 27–33.
  • S. Rajaram, and S. Agarwal ( 2005), ‘Generalization Bounds for k-Partite Ranking’, in NIPS 2005 Workshop on Learn to rank, Whistler, Canada, pp. 23–28.
  • C. Rudin, C. Cortes, M. Mohri, and R.E. Schapire (2005), ‘Margin-Based Ranking and Boosting Meet in the Middle’, in Proceedings of COLT, Bertinoro, Italy, pp. 63–78.
  • B. Scurfield (1996), ‘Multiple-Event Forced-Choice Tasks in the Theory of Signal Detectability’, Journal of Mathematical Psychology, 40, 253–269. doi: 10.1006/jmps.1996.0024
  • W. Waegeman, B.D. Baets, and L. Boullart ( 2008), ‘ROC Analysis in Ordinal Regression Learning’, Pattern Recognition Letters, 29, 1–9. doi: 10.1016/j.patrec.2007.07.019

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.