Cost-Sensitive Learning of SVM for Ranking
Cost-Sensitive Learning of SVM for Ranking
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DOI：
10.1007/11871842_86
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发表时间：
2006-09
期刊：
影响因子：
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通讯作者：
Jun Xu;Yunbo Cao;Hang Li;Yalou Huang
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文献类型：
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作者：
Jun Xu;Yunbo Cao;Hang Li;Yalou Huang
在本文中，我们提出了一种新的方法来学习排名。“Ranking SVM”是一种执行该任务的方法。它将问题形式化为实例对的二进制分类问题，并通过支持向量机（SVM）进行分类。在Ranking SVM中，不同秩对之间的实例对的不正确分类的损失被定义为相同的。我们注意到，在信息检索等许多应用中，在较高等级和较低等级之间犯错误的负面影响大于在较低等级之间犯错误。因此，很自然地将成本敏感学习的思想引入到学习排名中，或者更准确地说，为不同排名对之间的实例对的错误分类设置不同的损失。给定一个代价敏感的损失函数，我们可以在损失函数的基础上构建一个Ranking SVM模型。仿真结果表明，在实际的排序设置中，我们的方法比Ranking SVM效果更好。实验结果还表明，我们的方法可以优于现有的方法，包括排名支持向量机的真实的信息检索任务，如文档搜索和定义搜索。
In this paper, we propose a new method for learning to rank. ‘Ranking SVM’ is a method for performing the task. It formulizes the problem as that of binary classification on instance pairs and performs the classification by means of Support Vector Machines (SVM). In Ranking SVM, the losses for incorrect classifications of instance pairs between different rank pairs are defined as the same. We note that in many applications such as information retrieval the negative effects of making errors between higher ranks and lower ranks are larger than making errors among lower ranks. Therefore, it is natural to bring in the idea of cost-sensitive learning to learning to rank, or more precisely, to set up different losses for misclassification of instance pairs between different rank pairs. Given a cost-sensitive loss function we can construct a Ranking SVM model on the basis of the loss function. Simulation results show that our method works better than Ranking SVM in practical settings of ranking. Experimental results also indicate that our method can outperform existing methods including Ranking SVM on real information retrieval tasks such as document search and definition search.