2010 LearningwithCostIntervals

• (Liu et al., 2010) ⇒ Xu-Ying Liu, and Zhi-Hua Zhou. (2010). “Learning with Cost Intervals.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835857

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%22Learning+with+cost+intervals%22+2010
• http://portal.acm.org/citation.cfm?id=1835857&preflayout=flat#citedby

Quotes

Author Keywords

Cost-sensitive learning, cost interval, unequal cost

Abstract

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.

References

,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 LearningwithCostIntervals	Zhi-Hua Zhou Xu-Ying Liu			Learning with Cost Intervals		KDD-2010 Proceedings		10.1145/1835804.1835857		2010