Imbalanced classification in sparse and large behaviour datasets

DOI10.1007/s10618-017-0517-yzbMath1411.68124OpenAlexW2695033683MaRDI QIDQ1741360

Jellis Vanhoeyveld, David Martens

Publication date: 3 May 2019

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

Full work available at URL: https://doi.org/10.1007/s10618-017-0517-y

zbMATH Keywords

support vector machine (SVM)imbalanced learningcost-sensitive learningbehaviour dataon-line repositoryover-and undersampling

Mathematics Subject Classification ID

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

Uses Software

• UCI-ml
• LIBLINEAR
• SMOTEBoost
• GenLouvain
• NetKit
• MWMOTE
• AdaCost
• SMOTE
• ADASYN

Cites Work

• Cost-sensitive boosting for classification of imbalanced data
• Improved boosting algorithms using confidence-rated predictions
• Node classification over bipartite graphs through projection
• A neural network algorithm for semi-supervised node label learning from unbalanced data
• Communities in Networks
• Approximations of the critical region of the fbietkan statistic
• Benchmarking state-of-the-art classification algorithms for credit scoring
• Fast unfolding of communities in large networks
• Machine Learning: ECML 2004
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