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成本区间学习

Learning with Cost Intervals
课程网址: http://videolectures.net/kdd2010_liu_lci/  
主讲教师: Xu-Ying Liu
开课单位: 南京大学
开课时间: 信息不详。欢迎您在右侧留言补充。
课程语种: 英语
中文简介:
现有的成本敏感学习方法要求将不平等的错误分类成本作为精确值给出。然而,在许多实际应用中,通常很难获得精确的成本价值,因为用户可能只知道一种类型的错误比另一种类型严重得多,但是不能给出精确的描述。在这种情况下,使用成本间隔而不是精确的成本值更有意义。在本文中,我们报告了这一方向的第一项研究。我们提出了一种支持向量机cisvm方法来处理成本区间信息。实验表明,当只有成本区间可用时,使用最小成本、平均成本和最大学习成本,cisvm明显优于标准的成本敏感支持向量机。此外,考虑到在某些情况下,除了成本间隔(如成本分配)外,还可以获得其他有关成本的信息,我们提出了一种通用的方法,即使用分配信息来帮助改进性能。实验表明,该方法比标准的成本敏感支持向量机(假定期望成本为真值)降低60%以上的风险。
课程简介: Existing cost-sensitive learning methods require that the unequal misclassification costs should be given as precise values. In many real-world applications, however, it is generally difficult to have a precise cost value since the user maybe only knows that one type of mistake is much more severe than another type, yet it is infeasible to give a precise description. In such situations, it is more meaningful to work with a cost interval instead of a precise cost value. In this paper we report the first study along this direction. We propose the CISVM method, a support vector machine, to work with cost interval information. Experiments show that when there are only cost intervals available, CISVM is significantly superior to standard cost-sensitive SVMs using any of the minimal cost, mean cost and maximal cost to learn. Moreover, considering that in some cases other information about costs can be obtained in addition to cost intervals, such as the distribution of costs, we propose a general approach CODIS for using the distribution information to help improve performance. Experiments show that this approach can reduce 60% more risks than the standard cost-sensitive SVM which assumes the expected cost is the true value.
关 键 词: 成本间隔; 支持向量机; 代价敏感学习方法
课程来源: 视频讲座网
最后编审: 2019-12-26:cwx
阅读次数: 116