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.
Chapter PDF
Similar content being viewed by others
References
Busa-Fekete, R., Kégl, B., Éltető, T., Szarvas, G.: Ranking by calibrated AdaBoost. In: JMLR W&CP, vol. 14, pp. 37–48 (2011)
Cao, Z., Qin, T., Liu, T., Tsai, M., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24rd International Conference on Machine Learning, pp. 129–136 (2007)
Cesa-Bianchi, N., Lugosi, G.: Prediction, Learning, and Games. Cambridge University Press, New York (2006)
Chapelle, O., Chang, Y.: Yahoo! Learning to Rank Challenge Overview. In: Yahoo Learning to Rank Challenge (JMLR W&CP), Haifa, Israel, vol. 14, pp. 1–24 (2010)
Chapelle, O., Metlzer, D., Zhang, Y., Grinspan, P.: Expected reciprocal rank for graded relevance. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, pp. 621–630. ACM, New York (2009)
Chapelle, O., Wu, M.: Gradient descent optimization of smoothed information retrieval metrics. Information Retrievel 13(3), 216–235 (2010)
Cossock, D., Zhang, T.: Statistical analysis of Bayes optimal subset ranking. IEEE Transactions on Information Theory 54(11), 5140–5154 (2008)
Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research 4, 933–969 (2003)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55, 119–139 (1997)
Herbrich, R., Graepel, T., Obermayer, K.: Large margin rank boundaries for ordinal regression. In: Smola, B., Schoelkopf, S. (eds.) Advances in Large Margin Classifiers, pp. 115–132. MIT Press, Cambridge (2000)
Kégl, B., Busa-Fekete, R.: Boosting products of base classifiers. In: International Conference on Machine Learning, Montreal, Canada, vol. 26, pp. 497–504 (2009)
Li, P., Burges, C., Wu, Q.: McRank: Learning to rank using multiple classification and gradient boosting. In: Advances in Neural Information Processing Systems, vol. 19, pp. 897–904. The MIT Press, Cambridge (2007)
Mease, D., Wyner, A.: Evidence contrary to the statistical view of boosting. Journal of Machine Learning Research 9, 131–156 (2007)
Niculescu-Mizil, A., Caruana, R.: Obtaining calibrated probabilities from boosting. In: Proceedings of the 21st International Conference on Uncertainty in Artificial Intelligence, pp. 413–420 (2005)
Rissanen, J.: A universal prior for integers and estimation by minimum description length. Annals of Statistics 11, 416–431 (1983)
Robertson, S., Zaragoza, H.: The probabilistic relevance framework: BM25 and beyond. Found. Trends Inf. Retr. 3, 333–389 (2009)
Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Machine Learning 37(3), 297–336 (1999)
Valizadegan, H., Jin, R., Zhang, R., Mao, J.: Learning to rank by optimizing NDCG measure. In: Advances in Neural Information Processing Systems, vol. 22, pp. 1883–1891 (2009)
Wu, Q., Burges, C.J.C., Svore, K.M., Gao, J.: Adapting boosting for information retrieval measures. Inf. Retr. 13(3), 254–270 (2010)
Xu, J., Li, H.: AdaRank: a boosting algorithm for information retrieval. In: SIGIR 2007: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 391–398. ACM, New York (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Busa-Fekete, R., Kégl, B., Éltető, T., Szarvas, G. (2011). A Robust Ranking Methodology Based on Diverse Calibration of AdaBoost. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2011. Lecture Notes in Computer Science(), vol 6911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23780-5_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-23780-5_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23779-9
Online ISBN: 978-3-642-23780-5
eBook Packages: Computer ScienceComputer Science (R0)