Deriving optimal data-analytic regimes from benchmarking studies

DOI10.1016/j.csda.2016.10.016zbMath1466.62060OpenAlexW2525041088MaRDI QIDQ1658481

Iven van Mechelen, Antonio Calcagnì, Lisa L. Doove, Tom Frans Wilderjans

Publication date: 14 August 2018

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

Full work available at URL: http://hdl.handle.net/1887/72080

zbMATH Keywords

comparison of methodssupervised learningbenchmarkingclassification treesadditive clusteringdata-analytic regimes

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• R
• rpart
• HSAUR
• ElemStatLearn
• CHull
• ADPROCLUS

Cites Work

• Unnamed Item
• Bagging predictors
• Additive biclustering: a comparison of one new and two existing ALS algorithms
• Three-mode partitioning
• Algorithms for additive clustering of rectangular data tables
• GENNCLUS: New models for general nonhierarchical clustering analysis
• Testing for Qualitative Interactions between Treatment Effects and Patient Subsets
• A Statistical Framework for Hypothesis Testing in Real Data Comparison Studies

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