Abstract
The authors briefly review the theory of cost-sensitive learning, and the existing cost-sensitive learning algorithms. The authors categorize cost-sensitive learning algorithms into direct cost-sensitive learning and cost-sensitive meta-learning, which converts cost-insensitive classifiers into cost-sensitive ones. The authors also propose a simple yet general and effective meta-learning method called Empirical Threshold Adjusting (ETA for short). The authors evaluate the performance of various cost-sensitive meta-learning algorithms including ETA. ETA almost always produces the lowest misclassification cost, and is least sensitive to the misclassification cost ratio. Other useful conclusions on cost-sensitive meta-learning methods are drawn.
Original language | English |
---|---|
Pages (from-to) | 1203-1212 |
Number of pages | 10 |
Journal | Jisuanji Xuebao/Chinese Journal of Computers |
Volume | 30 |
Issue number | 8 |
State | Published - Aug 2007 |
Keywords
- Cost-sensitive learning
- Empirical Threshold Adjusting (ETA)
- Meta-learning