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ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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ICML '05 Proceedings of the 22nd international conference on Machine learning
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ICML '05 Proceedings of the 22nd international conference on Machine learning
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SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
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Information Processing and Management: an International Journal
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ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
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Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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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.