Cost-sensitive ensemble learning: a unifying framework

DOI10.1007/s10618-021-00790-4zbMath1494.68227arXiv2007.07361OpenAlexW3202026189MaRDI QIDQ832635

George Petrides, Wouter Verbeke

Publication date: 25 March 2022

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2007.07361

zbMATH Keywords

classificationclass imbalancecost-sensitive learningmisclassification cost

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Robust instance-dependent cost-sensitive classification

Uses Software

• R
• AdaBoost.MH
• C4.5
• SMOTEBoost
• XGBoost
• AdaCost
• SMOTE

Cites Work

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• Bagging predictors
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• Cost-sensitive boosting algorithms: do we really need them?
• 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
• A survey of cost-sensitive decision tree induction algorithms
• An Empirical Distribution Function for Sampling with Incomplete Information
• Robust classification for imprecise environments
• Random forests

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