A review of boosting methods for imbalanced data classification

DOI10.1007/S10044-014-0392-8zbMath1328.68169OpenAlexW2075267337MaRDI QIDQ903116

Qiujie Li, Yaobin Mao

Publication date: 5 January 2016

Published in: PAA. Pattern Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10044-014-0392-8

zbMATH Keywords

data samplingboostingcost-sensitive learningimbalanced data classification

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Uses Software

• UCI-ml
• SMOTEBoost
• AdaCost
• SMOTE

Cites Work

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• Greedy function approximation: A gradient boosting machine.
• Cost-sensitive boosting for classification of imbalanced data
• Boosting the margin: a new explanation for the effectiveness of voting methods
• Statistical behavior and consistency of classification methods based on convex risk minimization.
• Cost-sensitive learning and decision making for massachusetts pip claim fraud data
• Metalearning

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