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Published in:
2012 | OriginalPaper | Chapter
Towards Cost-Sensitive Learning for Real-World Applications
Authors : Xu-Ying Liu, Zhi-Hua Zhou
Published in: New Frontiers in Applied Data Mining
Publisher: Springer Berlin Heidelberg
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Many research work in cost-sensitive learning focused on binary class problems and assumed that the costs are precise. But real-world applications often have multiple classes and the costs cannot be obtained precisely. It is important to address these issues for cost-sensitive learning to be more useful for real-world applications. This paper gives a short introduction to cost-sensitive learning and then summaries some of our previous work related to the above two issues: (1) The analysis of why traditional Rescaling method fails to solve multi-class problems and our method Rescale
new
. (2) The problem of learning with cost intervals and our CISVM method. (3) The problem of learning with cost distributions and our CODIS method.