An empirical comparison of in-learning and post-learning optimization schemes for tuning the support vector machines in cost-sensitive applications

F. Tortorella
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引用次数: 2

Abstract

Support vector machines (SVM) are currently one of the classification systems most used in pattern recognition and data mining because of their accuracy and generalization capability. However, when dealing with very complex classification tasks where different errors bring different penalties, one should take into account the overall classification cost produced by the classifier more than its accuracy. It is thus necessary to provide some methods for tuning the SVM on the costs of the particular application. Depending on the characteristics of the cost matrix, this can be done during or after the learning phase of the classifier. In this paper we introduce two optimization schemes based on the two possible approaches and compare their performance on various data sets and kernels. The first experimental results show that both the proposed schemes are suitable for tuning SVM in cost-sensitive applications.
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成本敏感应用中支持向量机的学习中和学习后优化方案的经验比较
支持向量机(SVM)由于其准确性和泛化能力,是目前在模式识别和数据挖掘中应用最多的分类系统之一。然而,当处理非常复杂的分类任务时,不同的错误会带来不同的惩罚,人们应该考虑分类器产生的总体分类成本而不是其准确率。因此,有必要根据特定应用程序的成本提供一些方法来调优SVM。根据代价矩阵的特征,这可以在分类器的学习阶段期间或之后完成。在本文中,我们介绍了基于这两种可能的方法的两种优化方案,并比较了它们在不同数据集和内核上的性能。实验结果表明,这两种方法都适用于成本敏感的支持向量机调优。
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