期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 166, 期 1, 页码 212-220出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ejor.2004.03.031
关键词
decision analysis; cost-sensitive learning; classification
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. (c) 2004 Elsevier B.V. All rights reserved.
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