Abstract
In recent years, the algorithms of learning to rank have been proposed by researchers. Most of these algorithms are pairwise approach. In many real world applications, instances of ranks are imbalanced. After the instances of ranks are composed to pairs, the pairs of ranks are imbalanced too. In this paper, a cost-sensitive risk minimum model of pairwise learning to rank imbalance data sets is proposed. Following this model, the algorithm of cost-sensitive supported vector learning to rank is investigated. In experiment, the convention Ranking SVM is used as baseline. The document retrieval data set is used in experiment. The experimental results show that the performance of cost-sensitive supported vector learning to rank is better than Ranking SVM on the document retrieval data set.
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References
Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An Efficient Boosting Algorithm for Combining Preferences. In: 15th International Conference on Machine Learning, pp. 170–178 (1998)
Herbrich, R., Graepel, T., Obermayer, K.: Support Vector Learning for Ordinal Regression. In: Nineth Ann. Conf. Artificial Neural Networks (ICANN 1999), pp. 97–102 (1999)
Crammer, K., Singer, Y.: Pranking with Ranking. In: Fourteenth Ann. Conf. Neural Information Processing Systems, NIPS 2001 (2001)
Shashua, A., Levin, A.: Ranking with Large Margin Principle: Two Approaches. In: 16th Ann. Conf. Neural Information Processing Systems (NIPS 2003), pp. 961–968 (2003)
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to Rank Using Gradient Descent. In: The 22nd International Conference on Machine Learning, pp. 89–96 (2005)
Chu, W., Ghahramani, Z.: Preference Learning with Gaussian Processes. In: 22nd International Conference on Machine Learning, pp. 137–144 (2005)
Chu, W., Ghahramani, Z.: Gaussian Processes for Ordinal Regression. Journal of Machine Learning Research 6, 23 (2005)
Lin, H.-T., Li, L.: Large-margin thresholded ensembles for ordinal regression: Theory and practice. In: Balcázar, J.L., Long, P.M., Stephan, F. (eds.) ALT 2006. LNCS, vol. 4264, pp. 319–333. Springer, Heidelberg (2006)
Tsai, M.-F., Liu, T.-Y., Qin, T., Chen, H.-H., Ma, W.-Y.: FRank: A Ranking Method with Fidelity Loss. In: The 30th Annual International ACM SIGIR Conference (2007)
Pahikkala, T., Tsivtsivadze, E., Airola, A., Boberg, J., Salakoski, T.: Learning to rank with pairwise regularized least-squares. In: The 30th International Conference on Research and Development in Information Retrieval -Workshop on Learning to Rank for Information Retrieval, pp. 27–33 (2007)
Cao, Y., Xu, J., Liu, T.Y., Li, H., Huang, Y., Hon, H.-W.: Adapting Ranking SVM to Document Retrieval. In: 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 186–193 (2006)
Joachims, T.: Optimizing Search Engines Using Clickthrough Data. In: The ACM Conference on Knowledge Discovery and Data Mining, pp. 133–142 (2002)
Hersh, W., Buckley, C., Leone, T.J., Hickam, D.: OHSUMED: An Interactive Retrieval Evaluation and New Large Test Collection for Research. In: Seventeenth Ann. ACM-SIGIR Conf. Research and Development in Information Retrieval (SIGIR 1994), pp. 192–201|358 (1994)
Liu, T.Y., Xu, J., Qin, T., Xiong, W., Li, H.: Letor: Benchmark Dataset for Research on Learning to Rank for Information Retrieval. In: SIGIR 2007 Workshop on Learning to Rank for Information Retrieval (2007)
Kekalainen, J.: Binary and Graded Relevance in IR Evaluations - Comparison of the Effects on Ranking of IR Systems. Information Processing & Management 41, 1019–1033 (2005)
Raskutti, B., Kowalczyk, A.: Extreme re-balancing for SVMs: a Case Study. ACM SIGKDD Explorations Newsletter 6, 60–69 (2004)
Tao, Q., Wu, G.W., Wang, F.Y., Wang, J.: Posterior Probability Support Vector Machines for Unbalanced Data. IEEE Transactions on Neural Networks 16, 1561–1573 (2005)
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Chang, X., Zheng, Q., Lin, P. (2009). Cost-Sensitive Supported Vector Learning to Rank Imbalanced Data Set. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_33
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DOI: https://doi.org/10.1007/978-3-642-04020-7_33
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