Article ID Journal Published Year Pages File Type
9663711 European Journal of Operational Research 2005 9 Pages PDF
Abstract
In many real-life decision making situations the default assumption of equal misclassification costs underlying pattern recognition techniques is most likely violated. Then, cost-sensitive learning and decision making bring help for making cost-benefit-wise optimal decisions. This paper brings an up-to-date overview of several methods that aim to make a broad variety of error-based learners cost-sensitive. More specifically, we revisit direct minimum expected cost classification, MetaCost, over- and undersampling, and cost-sensitive boosting.
Related Topics
Physical Sciences and Engineering Computer Science Computer Science (General)
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