Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9663711 | European Journal of Operational Research | 2005 | 9 Pages |
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)
Authors
Stijn Viaene, Guido Dedene,