Cost-sensitive boosting for classification of imbalanced data
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Publication:996413
DOI10.1016/j.patcog.2007.04.009zbMath1122.68505OpenAlexW2103614420MaRDI QIDQ996413
Mohamed S. Kamel, Yanmin Sun, Yang Wang, Andrew K. C. Wong
Publication date: 14 September 2007
Published in: Pattern Recognition (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.patcog.2007.04.009
Database theory (68P15) Nonnumerical algorithms (68W05) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)
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Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Bagging predictors
- Cost-sensitive boosting for classification of imbalanced data
- A decision-theoretic generalization of on-line learning and an application to boosting
- Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
- Improved boosting algorithms using confidence-rated predictions
- 10.1162/15324430260185574
- Machine Learning: ECML 2004
- Supervised versus unsupervised binary-learning by feedforward neural networks
- Random forests
- Support vector machines for classification in nonstandard situations
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