Gradient boosting for high-dimensional prediction of rare events

DOI10.1016/J.CSDA.2016.07.016zbMath1464.62029OpenAlexW2510913895WikidataQ59676288 ScholiaQ59676288MaRDI QIDQ1658126

Lara Lusa, Rok Blagus

Publication date: 14 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2016.07.016

zbMATH Keywords

gradient boostingensemble classifiershigh-dimensional class-predictionrare events biasregularization through shrinkage and subsampling

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

New hard-thresholding rules based on data splitting in high-dimensional imbalanced classification ⋮ Advances in medical statistics ⋮ High dimensional classifiers in the imbalanced case

Uses Software

• R
• UCI-ml
• rda
• ElemStatLearn
• SMOTE
• PRMLT

Cites Work

• Greedy function approximation: A gradient boosting machine.
• Bagging predictors
• Regularized linear discriminant analysis and its application in microarrays
• Boosting ridge regression
• PRMLT
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Support-vector networks
• Breast Cancer Diagnosis from Proteomic Mass Spectrometry Data: A Comparative Evaluation
• Random forests
• Stochastic gradient boosting.
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