Hellinger distance decision trees are robust and skew-insensitive

DOI10.1007/s10618-011-0222-1zbMath1235.68141OpenAlexW2107542581MaRDI QIDQ408688

W. Philip Kegelmeyer, David A. Cieslak, T. Ryan Hoens, Nitesh V. Chawla

Publication date: 11 April 2012

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

Full work available at URL: https://doi.org/10.1007/s10618-011-0222-1

zbMATH Keywords

Hellinger distancedecision treeimbalanced data

Mathematics Subject Classification ID

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

Related Items (10)

Assessing the data complexity of imbalanced datasets ⋮ Bi-Invariant Dissimilarity Measures for Sample Distributions in Lie Groups ⋮ Classification Trees for Imbalanced Data: Surface-to-Volume Regularization ⋮ On the \(f\)-divergence for non-additive measures ⋮ AECID: asymmetric entropy for classifying imbalanced data ⋮ Enhancing techniques for learning decision trees from imbalanced data ⋮ Discrete-time survival forests with Hellinger distance decision trees ⋮ Handling imbalance in hierarchical classification problems using local classifiers approaches ⋮ A classification tree approach for the modeling of competing risks in discrete time ⋮ Polarized classification tree models: theory and computational aspects

Uses Software

• UCI-ml
• LIBSVM
• HandTill2001
• SMOTE

Cites Work

• Bagging predictors
• Tree induction for probability-based ranking
• Arcing classifiers. (With discussion)
• Improved boosting algorithms using confidence-rated predictions
• Random forests
• A simple generalisation of the area under the ROC curve for multiple class classification problems
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